最新刊期
卷 28 ， 期 4 ， 2024
- “在遥感数据应用服务领域，遥感产品真实性检验的重要性不言而喻。为了提升遥感数据产品的应用质量，确保服务的可持续性，国内外研究者多年来致力于建设遥感产品真实性检验站点。然而，由于陆表过程和观测目标的复杂性、遥感卫星载荷产品的多样性以及陆地遥感产品演变的动态性，全过程的理论运用与技术实现仍面临挑战。本文深入分析了遥感产品真实性检验地面观测场网、地面参考真值获取技术、遥感产品综合检验服务的国内外现状与发展趋势。在此基础上，文章从理论方法体系化入手，提出了在地统计学的空间变异理论、计量学的不确定性理论、运筹学的最优化理论等基础之上，研究真实性检验场网空间代表性表征、测量基准不确定性度量及传递、多途径检验结果综合定权合成、样本集与待检产品的最优匹配等方法，发展形成陆地观测卫星真实性检验场网方法体系框架。为应对国家空间基础设施重大工程建设和关键共性产品的应用需求，文章还提出了统筹建设完善真实性检验站网、航空协同真实性检验系统、基准参考构建及传递系统以及高精度检验服务系统的建议，以形成符合中国实际的陆地观测卫星遥感产品真实性检验技术方案。这项研究不仅有助于完善真实性检验场网的理论方法体系，还能促进国产陆地观测卫星遥感数据的充分利用，进一步提升遥感数据产品的定量化应用水平。对于推动遥感技术的发展和应用，具有深远的意义。”
摘要：Validation of remote sensing products, as a crucial process bridging remote sensing data products and their application services, is essential for meeting the increasing demands for precision and performance in a series of quantitative applications. Such a validation is also necessary for improving the algorithm and production procedure after collecting feedback in such applications.Over the years, numerous sites for the validation of land remote sensing products have been established domestically and internationally. However, the theoretical and technical systems in the construction of a validation network is still not perfect in the entire process. These systems include the site characteristic representation and selection, measurement of surface and/or atmospheric parameters, sampling of measurement, and validation service mode toward different users. The possible reasons include the complexity and dynamic nature of the Earth system that lead the interaction between different spheres, land surface heterogeneities and uniformities, system and random errors in measurements, and various types of remote sensing satellite products for different applications. These problems result in difficulties in fully utilizing existing facilities. Therefore， to solve a series of basic theories and techniques problems, and to provide new models and specific solutions become the precondition in improving the application efficiency of validation system infrastructure at present and in the near future.In this research context, the status and development trends are first analyzed, including the research domain of ground observation networks for validation, acquisition technology for ground reference truth, and comprehensive validation services for remote sensing products domestically and internationally. Thereafter, three basis theories (i.e., spatial variability theory in geostatistics, uncertainty theory in metrology, and optimization theory in operations research) are adopted to improve the validation methods. These three theories explore the spatial representativeness characterization of the network of product validation, uncertainty analysis and transfer of the fiducial reference measurements, comprehensive weighting and composition of multiple validation results, and optimal balance between the validation resources and user demands. Based on the theories, a framework is developed for the methodological system of the terrestrial observation satellite product validation network. This framework lays the theoretical foundation for the construction of a remote sensing production validation system and plays an important supporting role in forming targeted solutions. Aiming with the construction of the National Civil Space Infrastructure and the application needs of validation of key common products, considering overall layout, product coverage completeness, spatio-temporal consistency, and traceability, this study proposes specific requirements for the validation targets, validation areas, validation methods, validation accuracy, validation frequency, and service mode of Chinese land observation satellite remote sensing product validation network. The blueprint of the validation network for the “14th Five Year Plan” was designed based on the proposed methodology.This study coordinates the construction of a comprehensive product validation network, comprising an aerial collaborative validation system, a benchmark referenced transfer and fiducial reference measurement system, and a high accuracy validation service system. This network forms a technical solution for Chinese land observation satellite remote sensing product validation. The proposed solution will promote the resolution of related bottleneck issues, such as the accuracy, efficiency, and consistency of product validation. Lastly, the proposed solution will significantly improve the theoretical method system of the product validation network by fully utilizing and optimizing the use of domestic land observation satellite remote sensing data, and enhancing the level of quantitative applications.关键词：land observation satellite;remote sensing products;validation;fiducial reference measurements;service system;theoretical and methodology system- 518
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发布时间：2024-05-15 - “三维点云数据处理技术在自动驾驶、机器人和高精地图等领域的应用逐渐凸显。然而，当前处理方法主要依赖大规模高质量的标注数据集，且模型泛化性能有限，这成为了该领域的一大难题。为了应对这一挑战，学术界开始探索域自适应学习在点云数据处理中的应用。域自适应学习作为迁移学习的一个重要分支，旨在提高模型在不同域间的适应性。为此，本文系统性地梳理了近年来的三维点云域自适应学习方法，主要包括对抗学习、跨模态学习、伪标签学习和数据对齐四个方面。每种方法都有其独特的优势和面临的问题，这为后续研究提供了重要参考。总的来说，本文的研究不仅有助于更深入地理解点云域自适应学习领域，还为解决三维点云数据处理中的标注数据集需求和模型泛化问题提供了新的思路。未来，随着技术的不断进步，三维点云域自适应学习有望在更多领域发挥重要作用。”
摘要：Three-dimensional (3D) point cloud data have been widely used in many fields, such as autonomous driving, robotics, and high-precision mapping. At present, the state-of-the-art deep learning-based methods for 3D point cloud processing are mainly supervised learning methods. The performance of these methods depends heavily on large-scale, high-quality annotated datasets. However, annotating a large-scale, high-quality, category-diverse, and scenario-rich dataset is time-consuming and labor-intensive. In particular, obtaining sufficiently large numbers of samples for model optimization is also quite difficult in some special cases. In addition, 3D point cloud processing models trained on a single device in a special environment are difficult to generalize to different devices and environments. Their generalizability to various devices and environments is limited. Thus, how to reduce dependencies on high-quality annotated 3D point cloud datasets and how to improve the generalizability of current point cloud processing models are important research topics. In recent years, various kinds of impressive and elaborate technologies, such as meta-learning, few-shot learning, transfer learning, self-supervised learning, semisupervised learning, and weakly supervised learning, have been proposed to solve this problem. As an important research branch of transfer learning, domain adaptive learning aims to eliminate differences in feature distributions across domains and promote the generalization ability of deep learning models, thereby providing a novel solution to address this problem effectively. The academic community has conducted preliminary research on domain adaptive learning for point cloud processing. However, the domain adaptive learning field for point clouds still requires in-depth and effective exploration. Consequently, this study systematically summarizes and classifies recent 3D point cloud domain adaptive learning methods into five categories: adversarial learning, cross-modal learning, pseudo-label learning, data alignment, and other kinds of methods. First, we present the mathematical definition of the domain adaptive learning task and depict the chronological overview of the development of different domain adaptive learning methods to provide readers with a clear understanding. Second, we present the general solution for each category of domain adaptive learning methods and summarize the advantages and disadvantages of the current methods for each category. Third, we compare the performance of current methods on three-point cloud processing tasks, including 3D shape classification, 3D object detection, and 3D semantic segmentation. For each task, we also summarize the commonly used datasets and evaluation metrics for an intuitional comparison. Finally, we conclude the advantages and disadvantages of these five categories of methods and discuss future research directions about the 3D point cloud domain adaptive learning.关键词：remote sensing;3D point cloud;domain adaption learning;adversarial learning;cross-modal learning;pseudo-label learning;data alignment- 272
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发布时间：2024-05-15 -
Spatially constrained technology applications in information extraction from remote sensing images AI导读
“遥感卫星技术的飞速发展让高空间分辨率遥感图像在多个领域得到广泛应用。然而，这类图像通常面临类内方差较大的问题，影响了遥感信息的提取效果。针对这一挑战，空间约束在像素间的研究逐渐受到关注，并取得了一系列成果。但这些研究较为分散，缺乏系统性和联系。有专家对这些空间约束方法进行了全面的归纳和总结，涵盖了流程、应用场景和多种方法原理。通过对比各方法的优缺点，专家还展望了空间约束方法的发展趋势，并指出了研究中可能存在的不足。这一工作为遥感图像信息提取领域的研究提供了有价值的参考，有助于推动该领域的进一步发展。”
摘要：The problem of high intraclass variance is apparent in Very High spatial Resolution (VHR) remote sensing images. This problem limits the performance of many remote sensing information extraction methods. Consequently, Spatial Constraints (SCs) within image pixels have become a hot topic, resulting in many research results, but they lack associations and systems orientation from a general perspective. This study reviews and summarizes more than 100 related studies published in the past two decades to provide references for further research on information extraction in VHRs.In the second section, the SCs applications are divided into six scenarios (image matching, image segmentation, target detection, image classification, change detection, and others), and the implementation methods and characteristics of the main application scenarios are summarized. The SCs method is closely related to the specific application of the material. For example, SCs is mainly used to build descriptors and perform transformations in image matching; is implemented by model constraints, graph construction in space, and objective functions in image segmentation, target detection and image classification; and emphasizes the neighborhood between pixels and prior knowledge in change detection. The common feature of these scenarios is the development of a robust, unique, and representative descriptor via geometric space information, which can solve specific problems in images.In the third section, the SCs methods are divided into six types according to their implementation and principles (local templates, auxiliary references, spatial graph construction, model constraints, rule constraints, and others), and the advantages and disadvantages of the first five methods are compared. The results showed that the different SCs methods exhibited varying usability across application scenarios. (1) A local template uses the spatial information of the neighborhood and obtains more instances of stable information expression; thus, this approach is suitable for many application scenarios, especially image classification. (2) The point constraint in the auxiliary reference method relies on the spatial relations between feature points and often appears in image matching, while line constraints focus on the connection between the target and the linear object. Thus, this approach is suitable for extracting anthropogenic objects. Furthermore, surface constraints are spatially extensible and suitable for target detection. (3) Graph construction in space can intuitively and effectively extract multidimensional spatial information and is suitable for classifying hyperspectral images. (4) Model constraints are generalized in practical applications but rely on specific mathematical expressions. (5) Rule constraints can specify professional applications and are often used in image classification and change detection. Fully analyzing and considering application scenarios and specific problems are necessary for ensuring the effectiveness of SCs tools.In the fourth section, the development trends and possible shortcomings of SCs research are discussed. Specific suggestions for future work are also provided.This study has four sectionsIn the first section, the three stages of the SCs process (mining and expression of spatial information and construction of the SCs) are described in detail. The primary sources of spatial information were the neighborhood of pixels, imaging relations, and prior knowledge. The spatial information included the mean, median, extreme, and azimuth order. The SCs construction methods included objective functions, energy functions, and discriminant functions.关键词：spatial constraint;remote sensing image;information extraction;neighborhood;auxiliary constraint;remote sensing change detection;target extraction;land cover- 1075
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发布时间：2024-05-15
Reviews
- “地震灾害损失评估领域取得了重要进展。随着灾害风险的加剧和减灾救灾需求的提升，评估技术得到了快速发展。遥感技术作为大数据和智能化时代的代表，也在评估中发挥了重要作用。研究回顾了地震灾害损失评估的发展历程，对比了国内外评估软件平台的异同，总结了人口和经济损失估计的常用方法。同时，文章还深入探讨了震后遥感灾害损失评估以及基于遥感的震害预测方法的现状。然而，当前地震灾害损失评估工作仍面临一些问题。为此，文章提出了针对性的建议，并对遥感数据在地震灾害损失评估中的应用趋势进行了展望。这一研究不仅为地震灾害损失评估提供了新视角，也为未来减灾救灾工作提供了新的思路。”
摘要：Earthquake loss assessment is an important part of earthquake emergency preparedness, emergency response, and reconstruction. With the increased awareness of earthquake risk and the increasing demand for earthquake protection and disaster reduction, earthquake disaster loss assessment technology has undergone rapid development in recent years. Moreover, as remote sensing technology has entered the era of big data, remote sensing data are beginning to be widely used in earthquake loss assessment.This study reviews and summarizes the development of earthquake loss assessment and its use in remote sensing techniques. In particular, we initially reviewed the development of earthquake loss assessments and compared and analyzed the differences between loss calculation methods and the main functions of earthquake loss assessment software platforms at home and abroad. Second, we summarize the calculation methods for estimating casualties, injuries, and economic loss according to whether structural damage is considered. Third, we summarize the development of emergency earthquake loss assessment methods and earthquake loss prediction methods based on remote sensing. Finally, we analyzed the application prospects of NTL remote sensing data as a spatialization tool for population and GDP data for earthquake disaster loss.The following conclusions can be drawn. (1) In recent years, the calculation granularity of the domestic earthquake disaster loss estimation system has gradually improved. The applied structural damage estimation method has changed from the traditional empirical earthquake damage matrix to the fragile curve, and the system application scenario has developed from the post-earthquake period to full-time application. (2) A seismic loss assessment based on macro-data and historical earthquake cases is easy to calculate, but its reusability needs to be improved. Comparatively, various sophisticated methods for building loss information involve logical reasoning, but they are restricted by varying degrees of data completeness. (3) Post-earthquake loss assessment and earthquake damage prediction technologies based on remote sensing have gradually improved. The current development trends include methods for acquiring multisource remote sensing data and intelligent remote sensing data analysis. However, current earthquake loss assessment work is limited by incomplete data, which hinders the promotion of new methods, the lack of a unified national business platform, and the lack of uncertainty.The following suggestions are proposed(1) further develop the role of remote sensing data in the entire process of earthquake disaster loss estimation; (2) build a professional, high-quality, and national unified earthquake disaster risk management platform; and (3) enrich and develop the reporting mechanism of earthquake disaster assessment results from the perspective of the identified audience.关键词：earthquake damage estimation;earthquake risk assessment;loss assessment;earthquake damage assessment;remote sensing data- 1140
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发布时间：2024-05-15 - “一项关于中巴经济走廊洪扎河谷段滑坡灾害的研究取得了重要进展。该研究利用哨兵一号卫星数据和SBAS-InSAR技术，成功识别了53处潜在滑坡，并对其发育特征进行了深入分析。研究结果显示，洪扎河谷区不稳定形变阈值为-20 mm/a，潜在滑坡主要集中在河流两岸和公路上下边坡，且多分布在风化堆积层及变质岩区。此外，高差200-1000米、坡度30-40°以及坡向南、南西向是滑坡发育的优势地形条件。这一研究不仅有助于深化对洪扎河谷滑坡灾害的认识，还为巴基斯坦防灾减灾工作提供了科学依据，为确保中巴经济走廊的安全建设与运营提供了有力支持。”
摘要：The Hunza Valley in the China–Pakistan Economic Corridor (CPEC) in the northern part of Pakistan has a high relief and harsh geo-environment. Villages and towns in this area are prone to geohazard development, and high-risk incidents have been observed from the construction to operation stages of the CPEC. Landslide hazards in the Hunza Valley must be investigated and analyzed via landslide inventories and landslide development tools. This study applied 45 images and 42 images from the ascending and descending Sentinel-1A datasets, respectively, to monitor surface deformation via SBAS-InSAR. The deformation information along the slope direction was subsequently estimated. On the basis of the displacement rates derived from the SAR data, the optical remote sensing images were visually interpreted, and in situ surveys and validations were conducted. A total of 53 potential landslides were detected and delineated. On the basis of the effects of landslide identification and the detected deformation, image interpretation and validation features of typical large landslides Ghulmet and Humarri, 11 factors related to geomorphology, geology, hydrology, and vegetation were analyzed for landslide development. Maximum displacement velocities of -311 and -490 mm/a along the slope were detected on the basis of the ascending and descending datasets, respectively. Consequently, an annual deformation velocity of 20 mm/a was set as the threshold for the detection and mapping of potential landslides in the Hunza River Valley. The deformation of large landslides is severe under the influence of Hunza River erosion, and secondary landslides are developed. The validated potential landslides are distributed on the slopes on both sides of the Hunza River and are sometimes on the upper and lower slopes of the road. These active landslides primarily are developed in metamorphic rocks such as phyllite and slate. In the CPEC, landslides preferentially form and deform in areas where the elevation relief is between 200 and 1000 m, the slope is between 30° and 40°, and the aspect is within the southern and southwestern regions. Given the bare area of slope surfaces and sparse vegetation (NDVI<0.2), weathered and fragmented slopes provide enough provenance and materials for landslide development. The outcomes and results may facilitate hazard management and risk reduction in the Hunza Valley, allowing the operation of the CPEC to be uninterrupted. The findings of this work can also provide scientific references and data support for the monitoring and assessments of major landslide disasters that destroy roads and block rivers and their resulting secondary disaster events.关键词：China-Pakistan Economic Corridor;Hunza River valley;landslide;SBAS-InSAR;Earth surface deformation;early identification;development characteristics- 2126
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发布时间：2024-05-15 - “针对南水北调中线工程沿线煤炭开采沉陷对干渠的潜在威胁，科研团队利用Sentinel-1A数据和DS-InSAR技术，对焦作采空区进行了深入研究。研究揭示了焦作采空区2019年至2020年的地表形变情况，并构建了一套形变风险评估指标，实现了形变风险的分级。结果显示，焦作采空区内存在多个沉降盆地，但南水北调中线干渠焦作段整体受开采沉降影响较小。然而，张屯矿西北侧、白庄矿和中马村矿内存在较高形变风险区域，需持续监测以预防潜在沉陷灾害。这一研究不仅为南水北调灾害风险评估提供了科学依据，也为类似工程的安全管理提供了参考。”
摘要：As a national strategic project, the middle route of the South-to-North Water Diversion Project (SNWDP) is important for optimizing water resource allocation and promoting regional coordinated development in China. Unfortunately, the main canal of the middle route traverses a mining area in Jiaozuo. The massive surface subsidence caused by goafs left by coal mining has destroyed surrounding infrastructure and led to national economic losses. Deformation monitoring of the Jiaozuo section along the middle route of the SNWDP must be performed, and the threat of mining subsidence to the main channel should be assessed.The land surface of the Jiaozao Goaf is mostly covered with farmland and bare soil. Thus, obtaining a sufficient number of high coherence measurement points via traditional time series InSAR (TS-InSAR) is difficult. Consequently, TS-InSAR, which integrates PS and DS data (DS-InSAR), was used in this study. The TS-InSAR method benefits from homogeneous identification given its two-sample T-hypotheses and interferometric phase optimization based on an “eigen-decomposition-based maximum likelihood estimator” (EMI). The integration can efficiently improve the gathering of the spatial distribution density of measurement points with the help of a large number of scattered points. Particularly for this research, the spatiotemporal distribution of surface deformation in the Jiaozuo Goaf region from 2019 to 2020 was obtained from 54 Sentinel-1A images. Then, on the basis of the deformation monitoring results, a risk assessment indicator of deformation along the SNWDP that considers deformation and distance factors was developed. The index calculation results indicate that the study area can be divided into four grades according to the threat level of surface deformation to the main channel of the middle route of the SNWDP.The results further revealed several subsidence basins in the Jiaozuo Goaf that are distributed along the main canal of the middle route of the SNWDP. The maximum subsidence is 180 mm, and the maximum deformation rate is approximately -125 mm/a. The deformation of most subsidence centers has a continuous subsidence trend, but no evidence can prove that the boundaries of the subsidence basins would expand to the main channel of the SNWDP. The results of risk grading based on risk assessment indicators also revealed the presence of no-risk and low-risk areas along the main canal of the SNWDP, accompanied by a small number of medium- to high-risk areas.Overall, DS-InSAR can obtain sufficient observation points and realize fine deformation monitoring of mining areas within the SNWDP and Jiaozuo. The Jiaozuo section of the middle route of the SNWDP is less affected by mining subsidence, while high-deformation risk areas exist in the northwestern mines in Zhangtun, Baizhuang, and Zhongmacun. High-risk areas need to be continuously monitored to prevent potential subsidence hazards. The spatial distribution of medium- to high-risk areas is in good agreement with the results of the deformation analysis. Therefore, the risk indicator based on deformation and distance factors proposed in this study has good research value and can provide a scientific basis for disaster risk assessment of the SNWDP. Future research may include exploring hydrogeological factors and optimizing risk assessment indicators to improve the accuracy of deformation risk evaluation results under complex geological conditions.关键词：remote sensing;goaf;DS-InSAR;Surface deformation monitoring;middle route of South-North Water Diversion Project;risk indicator- 123
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发布时间：2024-05-15 - “我国云南省自然灾害频发，给当地人民带来了巨大的生命财产损失。为了更有效地进行救灾救援，有专家提出了一种基于无人机高分遥感图像和深度学习的目标检测技术，以快速定位损坏的建筑物。在损坏建筑物检测领域，目前面临两大挑战：一是高分辨率的震灾损坏建筑物数据稀缺且价格昂贵；二是待检测目标与背景及其他特征差异小，容易导致错检。为了克服这些问题，该专家构建了基于无人机遥感图像的大规模高分辨率震灾损坏建筑物数据集，涵盖了4598张遥感图像，并对目标建筑物进行了多形式标注。同时，该专家还提出了震灾损坏建筑物实时检测模型，其中融入了目标特征对齐模块、特征差异计算模块和目标边界约束的位置框检测模块。经过验证，该模型在震灾建筑物检测数据集上达到了86%的精度，并在不同地点的实际场景中得到了良好的应用效果。这一研究成果不仅为救灾救援提供了新的技术手段，也为无人机遥感图像在灾害监测领域的应用开辟了新方向。”
摘要：Natural disasters occur frequently in Yunnan, China and cause enormous losses of life and property. An object detection technology based on the deep learning of remote sensing images can be used to rapidly locate damaged buildings caused by natural disasters and subsequently aid with disaster relief. However, several challenges affect the detection of damaged buildings, such as the lack of data on earthquake-damaged buildings and the weakness of the features of objects to be detected. Thus, a UAV remote-sensing image-based largescale high-resolution earthquake-damaged building database (UEDB) was constructed. A total of 4598 remote sensing images were collected in the disaster area of Yangbi Yi Autonomous County in Dali Bai Autonomous Prefecture, Yunnan Province, China. The dataset includes 76,012 building instances, with each instance labeled in three formats: an object location box label, an object segmentation label, and an object boundary label. Then, a novel Earthquake-Damaged Buildings Real-time Detection Model (EDBRDM) was constructed. This model includes three modules: object feature alignment (OFAM), feature difference calculation (FDCM) , and object boundary constraint-based position box detection. The processing procedure of this model is as follows. Firstly, the OFAM correct the misalignment issues in images taken before and after a disaster, ensuring precise alignment of object features. This crucial step forms the foundation for subsequent feature analysis and difference calculation. Secondly, the FDCM is employed to compute the differences in features, highlighting the damage characteristics of buildings. By comparing the image features before and after the disaster, we can more clearly identify the damage of buildings, providing strong support for subsequent identification and analysis of damaged buildings. Lastly, the OBCPB introduces shallow boundary features into deep features, providing boundary constraints for the prediction of damaged building locations and categories. This step helps enhance detection accuracy, ensuring that we can accurately identify and locate damaged buildings. Through the collaborative effort of these three steps, we can achieve precise detection of damaged buildings. To validate the crucial role of the proposed modules, we delve into the internal operating principles of the model through the lens of feature visualization. Firstly, by comparing the feature changes after OFAM processing, we can clearly observe the significant improvements in the alignment of features across pre- and post-disaster images, demonstrating the effectiveness of OFAM in correcting image offsets. Secondly, by observing the enhancement of damaged building features by FDCM, we find that it effectively highlights the damaged areas of buildings, providing strong support for subsequent identification and analysis of damaged buildings. Finally, through the observation of the boundary constraint effect of OBCPB, we can see how it helps to improve the localization accuracy of the model, ensuring that damaged building objects can be accurately identified. It is noteworthy that our proposed model, EDBRDM, has achieved a remarkable accuracy of 86% on the UEDB test dataset, fully demonstrating its excellent performance. Furthermore, the application of EDBRDM to actual scenes in different locations has also yielded satisfactory results, further validating its effectiveness and reliability in practical applications.关键词：deep learning;high-resolution remote sensing images;object detection;change detection;earthquake disaster- 2731
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发布时间：2024-05-15
GeologyandDisasters
- “关于矿区生态系统服务功能的研究取得了重要进展。宁东大型煤炭基地作为研究案例，通过遥感数据驱动的方法，评估了气候调节、防风固沙、固碳释氧、水源涵养和土壤保持等五项生态系统服务功能的时空演变。结果表明，从2001年至2019年，宁东基地生态系统服务功能整体呈现改善趋势，但矿区生产活动对生态系统服务功能的增加产生了一定的迟滞作用。非矿区的生态系统服务功能上升速率普遍高于矿区，显示出煤炭开采活动对生态系统造成了一定的负面影响。这一研究不仅为矿区生态系统状况的遥感监测提供了新的视角，也为生态保护和修复提供了重要参考。未来，仍需进一步加大对生态系统保护和修复的力度，以应对气候变化和人类活动带来的挑战。”
摘要：Ecosystem Service Function (ESF) is the direct benefit obtained by human beings from the ecosystem. Thus, it is significant to monitor the ecosystem status from the perspective of ESF. Taking a large coalmining base Ningdong, China as the study area, we designed remote sensing-based methods to calculate five important ESFs: Climate Regulation (CR), Wind prevention and Sand fixation (WS), Carbon sequestration and Oxygen Release (CO), Water Conservation (WC), and Soil Conservation (SC). Subsequently, we capitalized on long-term data to evaluate the spatiotemporal variation of those ESFs and evaluate the effect of coal-mining on ecosystem from the perspectives of ESF. Results demonstrated that: (1) From 2001 to 2019, the ESF of the study area showed an overall improvement trend, where the CR, WS, and CO have a very significant increasing trend. The WC decreased slightly, and the SC were found basically unchanged. (2) With regard to the spatial distribution, the ESF is relatively high for areas far away from the coal-mining face, otherwise it is relatively low. (3) Contrast analysis between coalmining area and non-coalmining area indicated that coalmining impedes the increase of ESF in the study area. The increasing rate of CR in non-coalmining area are about twice that in coalmining area. The increasing rate of CO and WS in non-coalmining area is about 1.5 times that of the coalmining area. The overall improvement of ESF in the study area may be the comprehensive result of climatic environment change and artificial restoration activities. However, the overall improvement rate is significantly lower than that of non-mining areas, which implies that the coal mining activities have caused a certain negative impact on ESF and it is still necessary to strengthen the protection and restoration of the ecosystem.关键词：Coalmining base;remote sensing;ecosystem service function;dynamic monitoring;ecological environment;mining area;Mining impact- 1410
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发布时间：2024-05-15 - “在脱贫攻坚战的收官之年，中国对减贫效果的评估显得尤为重要。本研究通过独特的夜间灯光遥感数据，对全国831个贫困县和14个特困区的减贫情况进行了深入探索。结果显示，大部分贫困县经济水平显著提升，但仍有部分县份夜间灯光强度下降，主要分布在西部地区。此外，研究还发现了特困地区夜间灯光变化的四种类型，揭示了贫困县在特定区域的集聚和制约现象。特别值得关注的是，实施基础设施、特色产业、资产收益和易地搬迁等扶贫路径的贫困县，在夜间灯光变化上表现明显。这项研究不仅为我们评估减贫效果提供了新视角，也为解决相对贫困长效机制的建设提供了重要参考。”
摘要：Poverty is a major problem faced by developing countries. As the world’s largest developing country, China has been committed to poverty eradication. 2020 is the final year of China’s comprehensive victory in the war against poverty. At present, China has entered the post-poverty era, and the reasonable assessment of the poverty reduction effect is the focus of the acceptance work at this stage, which is of great significance to explore a long-term mechanism for solving relative poverty. The county-level geographical unit is the basic unit for China to formulate and implement the macro and micro policies and strategies for regional poverty reduction. Concentrated contiguous poverty-stricken areas concentrated in mountainous areas, old revolutionary base areas, and areas with poor natural resource endowment, with large internal development differences, belong to the most disaster-hit areas of poverty in China. After synthesizing an annual dataset of NPP-VIIRS nighttime light (NTL) data from 2014 to 2020, we developed a county-level NTL index to investigate the poverty reduction effects of 831 national level poverty-stricken counties and 14 concentrated contiguous poverty-stricken areas in China. The economic level of most poverty-stricken counties in China improved significantly during the study period, and the poverty reduction effect was prominent. However, 108 poverty-stricken counties still suffer from negative growth in terms of NTL intensity; these counties are located mainly at the junction of concentrated and contiguous poverty-stricken areas in the western region. The border area, mainly inhabited by ethnic minorities, has a poor ecological environment, a low level of economic development, and a relatively poor self-development ability, which may lead to a relatively poor poverty reduction effect. In addition, the NTL intensity development between the northern and southern parts of the western region is unbalanced. The growth rate of NTL in poor counties decreased from the east to the middle and western regions. In terms of the overall poverty alleviation trend, there was a period of rapid development in poor counties in the year before the declaration of poverty alleviation. However, after the declaration of poverty alleviation, the intensity of NTL decreased, the speed of poverty reduction slowed down, and there may be a risk of returning to poverty in some poor counties. Four NTL development modes, i.e., a small NTL base with a rapid growth rate (mode I), a large NTL base with a rapid growth rate (mode II), a large NTL base with a slow growth rate (mode III), and a small NTL base with a slow growth rate (mode IV), were identified in the 14 concentrated contiguous poverty-stricken areas. The high- and low-restriction modes were distributed at the junction areas of the different provincial administrative boundaries. In addition, poor counties along the border are vulnerable to marginalization. Further analysis indicated that significant NTL changes are apparent in the poverty-stricken counties, as demonstrated by their four poverty alleviation paths including infrastructure poverty alleviation, characteristic industry poverty alleviation, asset income poverty alleviation (photovoltaic poverty alleviation), and relocation poverty alleviation. However, the poverty reduction effect of poverty-stricken counties that take ecological compensation poverty alleviation, social guarantee poverty alleviation, and agricultural industry poverty alleviation as the leading poverty reduction methods are difficult to reflect in the NTL.关键词：nighttime lights (NTL);NPP-VIIRS;national level poverty-stricken counties;concentrated contiguous poverty-stricken areas;Spatiotemporal variations;poverty reduction- 1672
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发布时间：2024-05-15 - “工业热源遥感监测领域取得重要突破。针对热源特征不明、类型判定不准等问题，研究团队提出了一种耦合温度特征的工业热源人工神经网络遥感分类精准识别方法。该方法通过DBSCAN聚类算法和土地利用类型识别工业热源，利用频率统计方法建立温度特征模板，并构建人工神经网络模型进行热源类型判别。研究发现，不同工业热源温度频率与分布形态存在明显差异，主峰温度分别为795 K、830 K、760 K、1725 K。此外，该模型在工业热源分类识别上表现优异，训练集与验证分类识别精度分别高达99%和88.17%。研究还发现，我国工业热源时空分布呈现“地域集中”与“波动下降”双特征，主要集中在北方地区，数量占比高达85.4%。这一研究成果为基于卫星手段的大气工业污染源遥感监测提供了技术支撑，有望推动我国大气污染防治工作取得新进展。”
摘要：As one of main air pollution sources, the spatial-temporal distribution and category dependent determination of industrial heat sources are critical for policy making of air pollution control. However, due to the lack of identified characteristics, it is difficult to clearly differentiate the sub categories of the industrial heat sources in large geographical area using remote sensing technology. For that, we proposed a satellite-based Artificial Neural Network (ANN) identification method for industrial heat sources by coupling with temperature characteristics in this study by taking the whole China as a case. The Suomi-NPP Nightfire products containing location and temperature information in China from 2013 to 2020 were firstly collected and screened as industrial heat source clusters based on DBSCAN clustering algorithm and land use data. Then, four types of temperature characteristic templates depended on industrial heat source clusters were generated by combining the frequency statistical analysis with Gaussian function. Finally, a temperature characteristic template enhanced ANN model was developed to discriminate the sub categories of the recognized industrial heat sources and subsequently analyze their spatio-temporal changes. Results illustrate that there are significant differences in temperature frequency, distribution pattern and major-minor peaks among four types of industry heat sources (i.e. coal processing (CP), Metal Smelting and Rolling (MSR), Cement Lime and Gypsum Manufacturing (CLGM) and Refined Petroleum Products Manufacturing (RPPM)) with their major peak temperatures being 795 K、830 K、760 K and 1725 K, respectively. Moreover, with the enhancement of temperature characteristic template, the ANN model performs very well in identify the category depended industrial heat sources, with the training and verification accuracy of 99% and 88.17%, respectively. Besides, spatial-temporal distribution of industrial heat sources in China demonstrates the dual characteristics of “regional concentration” and “decreasing fluctuations”. Industrial heat sources are mainly concentrated in the northern region, accounting for 85.4% of the whole country. The main locations of CP, MSR, RPPM, and CLGM are Shanxi, Hebei, Xinjiang, and Anhui, respectively. In the period of 2013 to 2020, the overall trend of fluctuations is “descent - ascension - descent”, taking 2015 and 2018 as the turning time.There are obviously difference in temperature frequency, distribution pattern and distribution statistics among four types of industrial heat sources. Based on these differences, the temperature characteristic templates constructed are reliable and credible to discriminate the sub categories of industrial heat sources. Temperature characteristic template enhanced ANN model would provide a newly promising way for satellite-based precise identification of industrial heat sources by combining the temperature feature of industrial source and the super self-learning ability of ANN method.关键词：industrial heat source;temperature characteristic templates;Artificial neural Network;VIIRS;atmospheric remote sensing- 1200
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发布时间：2024-05-15
Ecology and Environment
- “一项关于植被指数的研究取得了重要进展。该研究针对增强型植被指数EVI（Enhanced Vegetation Index）在时间分辨率较低和云覆盖等影响下导致的大量像元缺失问题，提出了一种基于MODIS日地表反射率产品的日分辨率EVI重建方法。通过MVC（Maximum-Value Composite）与HANTS（Harmonic Analysis of Time Series）算法的结合，成功重建了黄淮海平原2021年的日分辨率EVI时间序列数据。研究结果表明，该重建算法不仅可用于大面积长时序日分辨率EVI时间序列数据的重建，而且重建结果纹理丰富，填补了原EVI大量的缺失像元，并去除了原EVI数据的噪声。与S-G滤波方法相比，经HANTS算法重建后的EVI在空间分布合理性以及保真性等方面均表现出优势，其重建EVI与优质EVI像元之间的年均R2与RMSE分别为0.94和0.024，优于S-G方法的0.73和0.093。这项研究为生成高时间分辨率EVI提供了新的思路和技术途径，对于植被监测、生态评估等领域具有重要的应用价值。”
摘要：The Enhanced Vegetation Index (EVI) combines factors such as atmospheric, soil, and saturation conditions and effectively correlates these data with vegetation biomass, leaf area index, and photosynthetically active radiation. Although the performance of the EVI is better than that of the Normalized Difference Vegetation Index (NDVI), the low temporal resolution of EVI products and the presence of cloud cover often result in a large number of missing pixels. In this study, we propose a daily resolution EVI reconstruction method that combines the Maximum Value Composite (MVC) and harmonic analysis of time series (HANTS) algorithms based on MODIS daily surface reflectance products.Given the spectral response differences of varying sensors carried by different satellites, the comparability of the EVIs calculated based on the Terra and Aqua satellites was analyzed prior to conducting the MVC operation. The analysis revealed a strong spatial linear correlation between the two variables, with R2 and RMSE values ranging from 0.9796-0.9935 and 0.0116-0.0297, respectively. The annual mean R2 and RMSE values were 0.9883 and 0.0196, respectively. The fitted parameters a and b had value ranges of 0.9447 to 1.0420 and -0.0065 to -0.0072, respectively, with annual mean values of 0.9910 and 0.0012. Despite spectral differences, the calculated EVIs based on the two satellite datasets exhibit minimal differences and thus are suitable for further processing via the MVC algorithm.This method was applied to reconstruct daily resolution EVI time series data for the North China Plain in 2021. The proposed EVI reconstruction algorithm is effective for large-scale and long-term reconstructions of daily resolution EVI time series data. The reconstructed EVI yields a rich texture, fills in the missing pixels, removes noise from the original EVI data, and follows the changing patterns of various land cover types. The HANTS method offers three advantages over the S-G filtering algorithm. First, compared with the original EVI, the HANTS method better preserved the spatial distribution patterns of the original EVI during reconstruction; by contrast, the S-G algorithm exhibited larger changes in spatial distribution in the reconstructed EVI. Second, the EVI curves reconstructed using the HANTS algorithm are smoother with minimal noise for typical land cover types; by contrast, the EVI curves reconstructed using the S-G algorithm have more local noise and nondifferentiable points, which hinders the extraction of vegetation phenological characteristics. Third, in terms of fidelity evaluation against high-quality reference EVI pixels, the HANTS algorithm demonstrated a strong linear correlation with the reference EVI pixels. The R2 and RMSE values ranged from 0.91 to 0.97 and from 0.017 to 0.032 across the months, with the strongest and weakest correlations occurring in September and June, respectively. By contrast, the S-G algorithm showed a weaker linear correlation with the reference EVI pixels. The R2 and RMSE values ranged from 0.38 to 0.91 and from 0.055 to 0.206 across the months, with the strongest and weakest correlations occurring in May and August, respectively. Overall, the HANTS method consistently outperformed the S-G method in terms of fidelity, with higher R2 values and lower RMSE values across all months. The proposed daily resolution EVI reconstruction method offers new guidelines and technical approaches for generating high-temporal resolution EVI data.关键词：MODIS;vegetation index;EVI;MVC;HANTS;daily resolution;North China Plain- 133
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发布时间：2024-05-15 - “本研究在卫星遥感领域取得了重要进展。研究团队基于植被辐射传输tau-omega模型，提出了相邻像元冠层微波透过率提取方法，并成功将其应用于卫星尺度。研究发现，森林冠层对被动微波遥感反演雪深存在不确定性影响，但通过森林辐射校正，可以有效提高雪深反演的精度。团队建立的森林透过率半经验估算模型，经过验证，其反演的透过率与参考值相关性高于0.7，且均方根误差RMSE较低。此外，研究还发现，经过森林辐射校正后，高低频亮温差与雪深的关系相关系数得到了显著提高。这项研究不仅为卫星遥感反演雪深提供了新的解决方案，也为林区的雪深监测提供了更为精确的参考和支撑。”
摘要：Spaceborne passive microwave remote sensing is a crucial technique for monitoring the global spatiotemporal distribution of snow depth. The forest canopy not only attenuates microwave radiation from the soil but also emits radiation into the sensor. Therefore, forest canopies increase the uncertainty of snow depth retrievals via passive microwave sensing. This research aimed to develop a microwave transmissivity model at the scale of satellite observations (0.25°×0.25°) to realize forest correction via satellite observations. The proposed novel method (hereafter referred to as the adjacent pixel approach) for estimating canopy transmissivity combines the radiative transfer functions of adjacent forests and open pixels. A semi-empirical transmissivity model based on forest biomass was built to correct satellite-observed brightness temperatures. The modeling brightness temperature data were compared with the AMSR2 observations in Northeast China to demonstrate the ability of the proposed transmissivity model to retrieve snow depth.As forest canopy effects were ignored by the microwave emission model, the brightness temperature was somewhat underestimated with respect to the satellite observations. By contrast, the proposed method corrected the information by using AMSR2 observations; hence, the model simulations were much closer to the AMSR2 observations. Then, the proposed semi-empirical microwave transmissivity model was further verified via the leave-one-out cross-validation method. The correlation coefficient between the estimates and reference values reached 0.7, and the RMSEs were 0.0589 and 0.0787 at 18.7 GHz and 36.5 GHz, respectively. The relationship between the brightness temperature spectral difference (Tb18.7V - Tb36.5V) and ground-based snow depth improved after forest correction, from 0.26 before correction to 0.46 after correction. An empirical retrieval algorithm was subsequently selected for testing to demonstrate the improvement in snow depth retrieval via forest radiation correction. The RMSE was 7.8 cm with forest radiation correction, whereas it was 8.9 cm without correction. Moreover, the correlation coefficient increased from 0.32 to 0.49.The proposed semi-empirical transmissivity method can significantly improve the performance of microwave radiative transfer models in forested areas. Moreover, this method can directly correct satellite-based brightness temperatures, thereby reducing the uncertainty of estimated snow depth values. This study provides a reference and guideline for improving snow depth under forest canopies.关键词：passive microwave remote sensing;forest canopy;adjacent pixel approach;microwave transmissivity;snow depth- 1284
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发布时间：2024-05-15 - “植被冠层结构参数研究取得了新进展。针对现有星载CI产品估算方法存在的精度问题，研究人员提出了一种动态选取混交林像元端元CI组分的方法，以改进针阔混交林植被聚集指数的估算精度。该方法利用国际地圈—生物圈计划的地表类型和描述二向性反射分布函数的地表各向异性平整指数进行双重约束，结合高分辨率的土地覆盖分类数据确定端元在像元中的面积比例，从而估算MODIS针阔混交林像元的聚集指数。研究结果表明，该方法可以显著改善针阔混交林像元CI值的估算精度，为针阔混交林CI产品生产和精度提高提供了可行的解决方案。这一研究成果对于全球碳、水循环研究以及植被生态学研究具有重要意义。”
摘要：The foliage Clumping Index (CI) is an important structural parameter of vegetation canopies. The CI influences radiation interception within canopies and plays an important role in the study of global carbon and water cycles. Currently, the widely used method for deriving satellite-borne CI products is based on a linear model constructed on the basis of the CI and the Normalized Difference between the Hotspot and Dark spot (NDHD) angular indices. As coniferous and broadleaf forests exhibit aggregate differences at the leaf scale, the CI inversion model can be applied to a variety of coefficients to generate different CI-NDHD models. Modelers typically use CI inversion coefficients of broadleaf forests to estimate the CI of coniferous-broadleaf mixed forests for medium-coarse resolution pixels, but this approach can theoretically cause a CI overestimation for this landcover type. Thus, in this study, we propose a novel coniferous-broadleaf Mixed Forest CI (MFCI) estimation method to dynamically select the endmember CIs of mixed forests pixel by pixel. The proposed method was successfully applied to satellite-borne MODIS data. The MFCI of the tree-farm study area on Saihanba was estimated, and the accuracy of the results was validated using ground-measured CIs.The MFCI was estimated by considering land cover classes and the Anisotropy Flatness Index (AFX), which describes the basic Bidirectional Reflectance Distribution Function (BRDF) variation. First, the prior values of the endmember NDHD were extracted pixel by pixel by imposing double constraints on the landcover type of the International Geosphere–Biosphere Program and the surface AFX, which characterize the shape of the BRDF. Then, the high-resolution land cover classification data were used to obtain the proportions of the endmembers in the coniferous-broadleaf mixed forest pixels. An optimization factor f was introduced to eliminate the differences between the NDHD of mixed forest pixels and the NDHD prior values of different vegetation cover types based on the NDHD linear mixing assumption. Then, the endmember CIs were calculated. Finally, the endmember CIs, combined with endmember abundance, were used to estimate the MFCIs based on Beer’s law.First, the existing MODIS CI product algorithm does not consider coniferous-broadleaf mixed forest pixels within mixed forest pixels, which leads to overestimation of coniferous–broadleaf mixed forest CIs. When the proportion of coniferous species reaches 60% in a mixed forest pixel, the overestimation of the CI can exceed 35%. Second, the proposed MFCI estimation method based on the CI-NDHD algorithm can significantly improve the CI estimation accuracy of coniferous-broadleaf mixed forest pixels. When the proportion of coniferous forest in the mixed forest pixels reached 60%, the accuracy improved by 28.03%. The root mean-square error and bias for the enhanced results were reduced by approximately 84% and 175%, respectively. Third, the MFCI method is more sensitive than the current MODIS CI products to changes in coniferous and broadleaf forest structures in mixed forest pixels.The current satellite CI products for mixed forest pixels typically use the modeled coefficients of broadleaf forests in the CI-NDHD model, which theoretically implies increased uncertainty in CI products. In this study, the proposed MFCI estimation method was used for coniferous-broadleaf forest mixed pixels. The CI endmembers were dynamically adjusted. The validation based on ground-measured CIs showed that the proposed method was significantly more accurate than the current MODIS CI products in terms of estimating the CI of mixed coniferous and broadleaved forests. In summary, the MFCI estimation method improved the estimation accuracy of mixed forest CI products in the selected study area. The proposed method is a promising technique for further improving the accuracy of global CI products.关键词：remote sensing;clumping index;Mixed Forest;MODIS;AFX;NDHD;MFCI- 1083
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发布时间：2024-05-15 - “一项关于飞机目标型号识别的研究取得了重要进展。该研究针对当前深度学习技术在精细化识别任务中的局限性，提出了一种融合目标分割与关键点检测的飞机型号识别方法。该方法通过结合多任务深度神经网络、条件随机场和模板匹配算法，实现了飞机型号的高精度识别。实验结果表明，与传统算法及完全端到端深度学习方法相比，该方法具有更高的准确率和实用性。该研究首先利用多任务深度神经网络迁移学习技术，实现了飞机目标物位置、掩膜与关键点信息的识别。接着，通过融合条件随机场的飞机目标掩膜精化算法和基于关键点的姿态调整算法，对识别目标的边界进行了精细化处理，并对机体姿态进行了调整。最后，在构建的飞机型号模板库基础上，将经过精化后处理的飞机掩膜信息与模板库进行匹配，从而实现了飞机目标的型号识别。这项研究的成果不仅为飞机目标型号识别提供了新的解决方案，也为深度学习技术在精细化识别任务中的应用开辟了新的方向。未来，该方法有望在航空安全、遥感影像分析等领域发挥重要作用。”
摘要：Aircraft detection via deep learning is a popular field in remote sensing image analysis. However, given the limited perspectives of satellite imagery and high similarities in image appearance, aircraft type recognition remains a challenging task. The existing deep learning methods cannot be satisfactorily applied to fine-grained aircraft type recognition tasks, which require refined labels for datasets. With the aim of effectively recognizing aircraft types in remote sensing images, we propose an integrated target segmentation and key point detection method for aircraft type recognition.The proposed method combines an organic multitask deep neural network with a conditional random field and template matching algorithm to achieve high-precision recognition of aircraft types by pretraining, fine-tuning, and postprocessing. First, we performed target aircraft position and mask and keypoint recognition by deploying multitask learning and transfer learning technology. Second, to facilitate high-precision template matching in the later stage, we utilized an aircraft target mask refinement algorithm and a keypoint-based mask attitude adjustment algorithm to achieve boundary refinement of the recognition target and aircraft target mask attitude adjustment. Finally, on the basis of the aircraft type template library constructed in this study, we matched the refined aircraft mask information with the template library to identify the aircraft type.The proposed algorithm was applied to the MTARSI dataset and remote sensing images for verification. The results showed that the recognition accuracy of the 11 types of images was 89%. Aircraft with simple structures and unique shapes, such as B-2 and B-1, exhibited high recognition accuracy, whereas aircraft with complex structures and high similarity with other shapes, such as E-3 reconnaissance aircraft, exhibited low recognition accuracy. Subsequently, the algorithm was compared with traditional algorithms and end-to-end deep learning methods. Eleven types of aircraft were studied. The results showed that the accuracy of our method was 15.4% and 20.7% better than those of the other two methods.The use of target segmentation and keypoint information has achieved good results in model recognition on high-resolution remote sensing images. However, limitations remain in terms of the breadth of identifiable aircraft types; therefore, further research is needed to address this research gap.关键词：object detection;segmentation;key points detection;conditional random field;aircraft type recognition- 1534
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发布时间：2024-05-15 - “针对变化检测领域长期以来面临的挑战，如噪声干扰和特殊地物影响导致的检测结果不准确，某研究团队提出了一种创新的非监督超像素级变化检测方法。该方法通过结合典型相关分析和直方图规定化，显著提高了变化检测的精度和稳定性。该团队首先对两个不同时刻的遥感影像进行预处理和超像素分割，然后基于超像素尺度和未变化概率计算权重。接着，通过超像素级多元变化检测和直方图规定化，精准地提取出变化特征。最后，利用权重影像、经典方法与变化特征进行决策融合，得到准确的变化检测结果图。为了验证该方法的有效性，研究团队在三个高光谱测试数据集和一个多光谱测试数据集上进行了实验。结果表明，该方法在四个测试数据集上的OA和Kappa指标均表现最优，且OA都达到了90%以上。相较于其他方法中的最高精度，本文方法的OA提高了4.41%、3.44%、1.74%和0.19%。这一研究成果为变化检测领域提供了新的解决方案，不仅提高了检测精度，还拓宽了遥感影像分析的应用范围。未来，该方法有望在环境保护、城市规划、灾害监测等领域发挥重要作用。”
摘要：Change detection, a critical task in remote sensing and geospatial analysis, involves the identification of areas where alterations in land cover types have occurred over time using multi-temporal images. The accurate detection of such changes is essential for various applications, including environmental monitoring, urban development assessment, and natural disaster management. However, existing change detection methods are often susceptible to noise and the influence of specific land features, resulting in significant speckle phenomena and reduced detection accuracy. These limitations hinder the reliable identification of change patterns in land cover, impacting the effectiveness of downstream analyses and decision-making processes.To address these challenges, this paper proposes an unsupervised superpixel-level change detection method that combines canonical correlation analysis and histogram matching. This method aims to improve the accuracy and reliability of change detection by addressing the limitations associated with traditional approaches. The proposed method consists of several steps. First, the remote sensing images were preprocessed and superpixel-segmented. This step is aimed at improving the quality of the image and dividing it into homogeneous regions called superpixels. Superpixel segmentation helps to preserve spatial information and reduces the influence of noise on subsequent analysis. Next, the weight of each superpixel was calculated based on the superpixel scale and the unchanged probability. Superpixel weights are used to highlight the importance of different regions in the change detection process. After obtaining the weights, the method proceeds to extract change features at the superpixel level using multivariate change detection and histogram matching. Multivariate change detection involves analyzing the spectral information of the superpixels to identify changes in land cover types. Histogram matching, on the other hand, aims to align the histograms of the superpixels from different time periods, enabling more accurate comparison and detection of changes. Finally, a change detection result map was developed based on the weighted image, classical methods, and change features.Three hyperspectral test datasets and one multispectral test dataset were used for experimental verification.Experimental validation of the proposed method was conducted on three hyperspectral test datasets and one multispectral test dataset. The results demonstrate the superior performance of the proposed method, with the Overall Accuracy (OA) and Kappa index surpassing those of existing methods across all four test datasets. Specifically, the OA values consistently exceed 90% on all datasets, indicating the high accuracy and robustness of the proposed method. Moreover, comparative analysis reveals significant improvements in the OA when compared to other existing methods. The proposed method achieves an OA increase of 4.41%, 3.44%, 1.74%, and 0.19% on the four datasets, highlighting its efficacy in enhancing change detection accuracy and reliability. In conclusion, the proposed unsupervised superpixel-level change detection method, which integrates canonical correlation analysis and histogram matching, demonstrates remarkable performance in detecting changes in land cover types from multi-temporal remote sensing images.关键词：remote sensing;super pixel;change detection;canonical correlation analysis;histogram specification- 1364
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发布时间：2024-05-15 - “针对微纳卫星在轨计算平台上的目标识别问题，研究团队提出了一种深度可分离卷积神经网络模型。该模型通过改进Yolov4网络结构，降低了整体网络结构的深度与复杂度，并利用可分离卷积结构减少模型参数量。同时，通过合并卷积层与Batch Normalization层，进一步加快了前向推理速度。此外，研究团队还借鉴Focal损失函数的思想，改进了目标检测网络的损失函数，以缓解前景与背景样本比例不均衡的问题。与原Yolov4网络模型相比，新模型在保证94.09%识别精度的前提下，参数量降低了约7倍，FLOPs降低了约30倍。此外，通过与Yolo系列、SSD、MobileNet、CenterNet等前沿网络模型的对比实验，进一步验证了新模型的算法性能。这一研究成果为实现在轨目标识别与过滤无用数据提供了理论支撑，有助于推动微纳卫星在轨计算平台的技术进步和应用拓展。”
摘要：As an important transport carrier and military target, aircraft detection in remote sensing images is important for aircraft rescue, early warning, and other fields. At present, the widely used neural network model has a complex structure and requires a large number of parameters, which limits the computing and storage resources of aircraft detection satellites. The efficiency and accuracy of satellite in-orbit detection need to be studied, and the computational structure must be optimized. Using neural networks in lightweight operation can reduce the computational costs and compress the overall framework.In this study, on the basis of a deep separable convolution neural network combined with deep separable convolution, the SwishBlcok bottleneck module was established by referring to the construction idea of a reverse residual structure. The characteristics of the network were simultaneously expanded in three aspects as follows: ResBlock_body was replaced with the overall design idea of the main framework of YOLO v4. Simultaneously, the channel attention mechanism of SENet was used for reference and integrated into the network structure. Different weights were given to the extracted feature maps and information. On the premise of maintaining channel separation, a separable convolution structure was used to improve the SPP structure and PANet structure; in this manner, both the number of model parameters and the memory dependence could be reduced. Moreover, the convolution layer and the batch normalization layer were merged to further accelerate forward reasoning. Drawing on the focal loss function, the loss function of object detection was improved to solve the imbalance between foreground and background data samples.The quality of algorithm restoration necessitates verification. In this study, objective evaluation indices were used to measure the algorithm from multiple angles. The public RSOD dataset and an internally produced dataset were used to compare the high-performance network models for algorithm verification. In terms of the rationality of the various improvements in the network model, verification experiments were conducted to measure the quality and processing speed of the algorithm. Then, the trained model was deployed on an embedded platform to verify the detection speed of the improved YOLO v4 algorithm model for on-orbit object recognition. The number of parameters of the proposed scheme was reduced by sevenfold compared with that of the original method, and the number of FLOPs was reduced by approximately 30 at a recognition accuracy of 94.09%. Subsequently, the experimental results were compared with the findings for the YOLO series, SSD, MobileNet, CenterNet, and other cutting-edge network models. The proposed algorithm outperformed the other methods.The proposed on-orbit object detection model can overcome the limitations of computing and storage resources, which traditionally cannot support high-precision complex models. The experimental results from ground and embedded platforms also prove that the proposed on-orbit object detection algorithm can effectively detect remote sensing targets based on detection performance. Future research may expand the scale of remote sensing datasets and improve the universality of model application scenarios关键词：remote sensing;convolution neural network;Yolov4;Optical Remote Object detection;Reverse residual structure- 966
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发布时间：2024-05-15 -
CBAM UNet+++： Attention mechanism to guide change detection studies of full-scale connected networks AI导读
“针对普通跳跃连接在遥感影像变化检测中的局限性，以及编码器特征提取能力不足的问题，研究团队提出了一种新型的CBAM UNet+++网络结构。该网络结合了高分辨率遥感影像的变化检测需求，通过引入耦合注意力机制CBAM，有效提升了网络对全尺度变化信息的捕捉能力和编码器对显著特征的学习能力。在实验中，研究团队利用两种不同变化类型的高分辨率遥感影像变化检测数据集进行验证。结果显示，CBAM UNet+++在LEBEDEV多地物变化数据集上取得了最高精度，F1和OA值分别达到了88.9%和97.3%。而在LEVIR-CD建筑物变化数据集上，也获得了次高精度，F1和OA值分别为86.7%和96.8%。这些成果证明了CBAM UNet+++在遥感影像变化检测中的优越性能。此外，研究还发现，CBAM UNet+++能够有针对性地获取深层语义信息，定性结果优于其他基准网络。这一发现为遥感影像变化检测领域的研究提供了新的思路和方法，也为后续研究奠定了基础。”
摘要：Existing change detection networks rely heavily on layer-by-layer convolution for feature extraction. However, the use of this method leads to a loss of information, and it lacks the ability to mine important change features. Therefore, knowing how to effectively suppress the influence of the background and identifying ways to increase the ability of the network to learn salient features and generate recognizable feature information are highly important for change detection tasks. Traditional skip connections lack the ability to obtain change information from a full-scale perspective and perform encoder feature extraction. Thus, a UNet+++ high-resolution remote sensing image change detection network called CBAM UNet+++ combined with a coupled attention mechanism (i.e., a convolutional block attention module [CBAM]) was designed in this research.CBAM UNet+++ is based on the semantic segmentation structure UNet+++. The unique full-scale concatenation operation of UNet+++ effectively fuses the semantic and spatial information from the full-scale perspective to avoid information loss. The basic convolutional unit can be replaced by a residual attention module (Residual Block_CBAM and ResBlock_CBAM) to suppress background effects and enhance the learning ability of the encoder to handle significant features. The residual attention module was validated on two remote sensing image change detection datasets—LEBEDV and LEVIR-CD—involving different high-resolution change regions.The proposed method has the highest accuracy on the LEBEDEV multifeature change dataset, with F1 and OA values of 88.9% and 97.3%, respectively, and the second highest accuracy on the LEVIR-CD building change dataset, with F1 and OA values of 86.7% and 96.8%, respectively. The proposed method can obtain deep semantics in a targeted manner, and its qualitative results are better than those of other benchmark networks.The CBAM UNet+++ method can accurately locate and detect change regions with better detection and accuracy than can the benchmark method. The accuracy results of the two selected datasets were slightly different, but they were not inconsistent. The accuracy of the CBAM UNet+++ model was disrupted by pseudochange information in the building dataset. Future work may focus on the usability of this network for change detection in heterogeneous dual-temporal images to further address the impact of early fusion on change detection accuracy.关键词：remote sensing;change detection;UNet+++;attention mechanism;encoding and decoding- 1602
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发布时间：2024-05-15 - “针对多时相、多极化SAR数据的超像素生成技术，研究取得了重要进展。针对单时相超像素分割方法无法充分利用地物时序完整散射信息的问题，研究人员提出了一种基于简单线性迭代聚类（SLIC）模型的多时相极化SAR影像自适应超像素生成方法。该方法结合多个时相的极化协方差矩阵，基于Wishart分布计算时序极化SAR相似性距离，同时利用多时相极化SAR数据进行梯度计算和边缘检测。通过引入基于多时相极化SAR边缘检测的同质性测度因子，该方法能够自适应平衡极化距离和空间距离的权重关系。实验结果表明，该方法在可视化效果和定量精度上均优于单时相极化SAR超像素生成方法和现有的多时相极化SAR超像素方法，超像素能够紧密贴合研究区域地块边界。这一研究成果为对象级数据处理体系的高效处理和应用提供了新的解决方案，对于大数据量的多时相、多极化SAR数据的处理和应用具有重要意义。”
摘要：Superpixel generation is an important pre-processing step in the object-level data processing system, which is of great practical significance for the efficient processing and application of multi-temporal and multi-polarized SAR data. The single-temporal superpixel segmentation method does not fully utilize the complete scattering information of the segmented objects in the time series. To address this problem, this paper proposes a multi-temporal PolSAR Images adaptive cooperative segmentation method based on the Simple Linear Iterative Clustering (SLIC) model, which takes full use of the advantages of fully observed and describable time-varying characteristics of the time-series PolSAR data.Firstly, this method calculates the time-series PolSAR similarity distance based on Wishart distribution by uniting the polarization covariance matrix of multi-temporal; then uses multi-temporal polarization SAR data to perform gradient calculation to detect image edges; Finally, a homogeneity measure factor based on multi-temporal polarimetric SAR edge detection is proposed to adaptively balance the weight relationship between polarimetric distance and spatial distance.In this paper, we used 8 Radarsat-2 quad-polarization SAR images to evaluate the effectiveness of this method in terms of both visualization effect and quantitative accuracy. The results show that the method in this paper outperforms the single-temporal PolSAR superpixel generation method and the existing traditional multi-temporal PolSAR superpixel method. For example, as for the superpixel generation result with the quad-polarization SAR data (K = 12000), the value of the boundary recall(BR) and the achievable segmentation accuracy(ASA) by the proposed similarity measure and homogeneity factor is about 93.58% and 95.13%, respectively.To address the problem that the single-temporal polarization SAR segmentation does not consider the time-varying characteristics of the ground object polarization characteristics, this paper proposes a multi-temporal polarization SAR image adaptive collaborative segmentation method based on the SLIC model. The experimental results show that compared with the segmentation method based on single-temporal data and the traditional multi-temporal polarimetric SAR superpixel segmentation method, the superpixels generated by this paper have obvious advantages in both visualization effect and quantitative accuracy, and can effectively fit the ground truth boundary, which proves that the proposed method is an effective superpixel generation method.关键词：remote sensing;PolSAR;image segmentation;SLIC;superpixels;multi-temporal- 1409
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发布时间：2024-05-15 -
Dual-threshold registration based on surface fitting of aerial filter array multispectral images AI导读
“针对航空滤光片阵列多光谱图像配准过程中的精度问题，有学者提出了基于匹配点位置差曲面拟合双阈值剔除方法。该方法选取多光谱中间波段图像作为基准，利用SIFT算法提取匹配点，并通过计算位置差、构建Delaunay三角网、IDW算法拟合位置差曲面等步骤，构建了位置差三维阈值空间。最终，利用该空间筛选出精确匹配点，完成图像配准。理论分析和实验结果均证明，该方法能有效提高航空滤光片阵列多光谱图像配准的精度，为相关领域的研究提供了新的解决方案。”
摘要：Aerial filter array multispectral images and their high precision registrations are important for guaranteeing subsequent image processing and application. In the process of image registration, the position accuracy of matching points is important in determining the accuracy of image registration. However, objects of different strips in the same band image are acquired at different moments, the image displacement between single-band images is large, and the difference in geometric errors of matching points between topographic undulating areas and flat areas in the image is obvious. Additionally, false matching points cannot be accurately eliminated by the global matrix. The difficulty of eliminating mismatched points in multispectral images of aerial filter arrays must be addressed because of the displacement of image points between spectral segments. Thus, a new method of double-threshold elimination based on matching point position difference surface fitting is proposed in this study.First, the intermediate band image of the filter array multispectral images was selected as the reference image, and the matching points in the reference image and the image to be registered were extracted by the subpixel-level SIFT algorithm. Second, the difference in the positions of the matching points of the two bands was calculated point by point at the matching points of the benchmark image, and the Delaunay triangulation network of matching points in the reference image was constructed. The position difference surface was smoothed, the position differences between the matching points of the reference image and the corresponding matching points of the image requiring registration were calculated point by point, and a certain tolerance range was shifted upward and downward to form a 3D position difference threshold space. Finally, accurate matching points were selected using the 3D threshold space of the position difference to complete the registration.The three-band composite image of the algorithm-registered image in this study presented clear features and well-defined details and met the requirements of subsequent data processing and application. The effectiveness of the proposed algorithm was illustrated by registering two datasets of filter array multispectral images, from which qualitative and quantitative perspectives were verified. Regarding false color, the composite image processed by the proposed algorithm did not show obvious pseudoedges, and the features were clear. However, pseudoedges were obvious in the comparison algorithm and difference image grayscale histograms. Among the experiments of the two datasets, the difference image histogram curve of the proposed algorithm presented the largest shift to the left. The image registered by the proposed algorithm had the smallest difference from the reference image and the best registration effect.Theoretical analysis and experimental results show that the dual-threshold pointing algorithm based on matching point difference fitting of curved surfaces can screen high-precision matching points in aerial filter array multispectral images and effectively improve the accuracy of image registration. Surface fitting to the position difference of matching points can help reveal the trend of image point displacement in each region. This scheme can also effectively eliminate false matching points around the correct matching points, especially since the image displace.关键词：remote sensing;Curved surface;fitting Double threshold;Filter array;multispectral image;registration- 820
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发布时间：2024-05-15 - “资源三号03星是我国高分辨率立体测绘卫星，其在全球范围1：5万比例尺测图应用中发挥着重要作用。为了提升卫星影像的高程精度，该卫星搭载了单波束激光测高仪，获取高精度激光测高点。有研究表明，通过联合区域网平差处理方法，利用激光测高点与立体影像的结合，可以大幅提升立体影像的高程精度。试验中，黑龙江省中部地区和河北省太行山地区的ZY3-03星三线阵立体影像的高程中误差均得到了显著降低，满足了我国1：5万比例尺立体测图的精度要求。这一研究成果为提升我国卫星遥感技术水平和地理信息资源建设提供了有力支持。”
摘要：When using domestic satellites for global geographic resource construction, one of the most important promises is to improve the geometric accuracy of satellite imagery lacking Ground Control Points (GCPs). The Ziyuan3-03 (ZY3-03) satellite, launched on July 25, 2020, was the third high-resolution stereo mapping satellite of the Ziyuan3 series. ZY3-03 is mainly used for the global application of 1∶50000-scale mapping. Triple linear-array push-broom panchromatic cameras with a resolution of ≤2.5 m and a multispectral camera with a resolution of 5.8 m were loaded on this satellite. Moreover, an additional single-beam laser altimeter was loaded on the ZY3-03 satellite to improve its vertical accuracy. This addition enables satellites to simultaneously obtain high-vertical-accuracy Laser Altimetry Points (LAPs). The integration of stereo-images and LAPs for improving the vertical accuracy of stereo-images is considered the key to realizing the application of 1∶50,000-scale stereo mapping of ZY3-03 images with sparse or no GCPs.A combined block adjustment method involving ZY3-03 triple linear-array stereo-images and synchronous orbit LAPs was proposed to improve the elevation accuracy of images. First, a method for accurately obtaining the image coordinates of LAPs on stereo-images was designed. Given the slight differences in relative planar errors between LAPs and synchronous nadir images, the image coordinates of LAPs on synchronous orbit nadir images could be correctly acquired using the image rational function model (RFM) and the ground geodetic coordinates of the LAPs. Then, accurate pixel coordinates of the LAPs on the triple linear-array stereo-images were determined via high-precision image matching between the forward/backward images and nadir images. The use of whole-orbital stereo-images as the operation unit allowed for the construction of a combined block adjustment model based on RFM and a combined block adjustment strategy.A total of 12 ZY3-03 satellite triple linear-array stereo image pairs and 81 synchronous orbit LAPs of plain and hilly terrains in Heilongjiang Province, China, were selected as the experimental data. Simultaneously, 270 global positioning system (GPS) points were collected as checkpoints. For the combined adjustment between stereo-images and LAPs, the vertical root mean square error (RMSE) of the images was reduced from 5.27 to 2.58 m. Then, seven ZY3-03 satellite triple linear-array stereo image pairs and six synchronous orbit LAPs of mountainous terrains in Hebei Province were selected as experimental data, with 115 GPS points taken as checkpoints. After performing the combined block adjustment, the vertical RMSE of the images decreased from 11.25 to 4.45 m. The experimental results indicate that the vertical accuracy of the ZY3-03 images increased significantly. The obtained data can satisfy the precision requirements of 1∶50,000-scale stereo mapping in China.The combined block adjustment method can be effectively implemented regardless of the terrain (i.e., flat, hilly, or mountainous). The vertical accuracy of stereo-images can be greatly improved to satisfy the accuracy requirements of 1∶50000-scale stereo mapping in China.关键词：remote sensing;Ziyuan3-03 satellite;triple linear-array stereo imagery;laser altimetry point;combined block adjustment;geometric accuracy- 1082
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发布时间：2024-05-15
Models and Methods
- “一项关于海洋浮游植物群落结构的研究取得了重要进展。该研究利用高斯分解法，基于2016年至2018年渤海、黄海和东海的航次调查数据，成功开发了一种基于浮游植物吸收的色素浓度遥感模型。这一模型通过原位观测数据集的评估，误差可接受，且卫星匹配验证也显示反演结果与实测数据的一致性。利用该模型，研究团队进一步分析了SeaWiFS和MODIS-Aqua遥感反射率月平均产品（1998年—2020年），获得了渤海、黄海和东海区域16种色素浓度23年的时空变化数据记录。这一研究成果不仅为复杂浑浊沿海水域中的浮游植物色素浓度研究提供了重要手段，还为海洋浮游植物群落结构的精细化研究提供了有力的数据支撑。该研究对于深入了解和研究碳循环和气候变化具有重要意义，为相关领域的科学研究和实践应用提供了新的思路和方向。”
摘要：Studying marine phytoplankton communities is essential for understanding the carbon cycle and climate change. Phytoplankton pigments can describe the composition and physiological state of phytoplankton communities. Detecting phytoplankton pigment concentrations is also important, and remote sensing technology permits macroscopic long-term series monitoring of phytoplankton pigment concentrations. However, existing studies still have limitations. First, remote sensing methods for retrieving additional types of pigments are lacking. Existing studies have focused primarily on a few pigments or pigment groups. Second, the existing pigment inversion algorithms are mostly based on oceanic water data, and studies of optical class II waters off China are insufficient. Finally, satellite remote sensing datasets for long time series of multiple phytoplankton pigment concentrations in phytoplankton-related fields are lacking, indicating low data support. In this study, phytoplankton absorption data, 16 pigment concentration data points, and remote sensing reflectance data were collected. A total of 7 cruise experiments were performed in the Bohai Sea, Yellow Sea, and East China Sea from 2016 to 2018. Then, a remote-sensing model and a long-term series dataset of the spatiotemporal distribution of phytoplankton pigment concentrations were developed.The remote removal of fine particulate matter was achieved by determining the relationship between phytoplankton absorption and the 16 pigments. The measured absorption coefficients were decomposed into Gaussian functions, and the relationship between the Gaussian parameters and the measured pigment concentration was analyzed to construct inversion models. A two-component model of phytoplankton size classes was also used to determine hyperspectral phytoplankton absorption. The performance of the models was evaluated for consistency. Then, the models were assessed using in situ datasets and leave-one-out cross-validation methods. The results showed competitive and acceptable error results, with Mean Absolute Percentage Errors (MAPEs) of less than ~60% for most pigments. Satellite-measured validation also produced promising prediction errors, yielding MAPEs in the range of 40%—60% for most pigments. Finally, the developed models were applied to the SeaWiFS and MODIS-Aqua remote sensing reflectance monthly mean products (1998—2020) to obtain 23 years of spatiotemporal patterns of 16 pigment concentrations in the Bohai Sea, Yellow Sea, and East China Sea.The satellite remote sensing dataset revealed 16 similar pigment distribution patterns, revealing a decreasing trend from nearshore to offshore waters. In the Bohai Sea, the pigment concentration is high in winter and spring and low in summer. In summer, the pigment concentration peaks in the coastal areas of Jiangsu Province and gradually decreases toward Zhejiang and Fujian Provinces. A triangular high concentration is apparent in the Yangtze River Estuary, with the area extending from west to east in autumn and winter. The phytoplankton pigment concentration was relatively low in the outer deepwater area, and the variation in concentration with season was only slight.The remote sensing datasets of 16 phytoplankton pigment concentrations can be downloaded fromhttps://doi.org/10.17632/bhcznf2m7v.1. In related fields, scholars can study the macroscopic and continuous phytoplankton community structure monitoring and physiological characteristics of phytoplankton in the Bohai Sea, Yellow Sea, and East China Sea based on information from pigment concentration remote sensing datasets. This dataset can enrich the understanding of marine phytoplankton pigment distributions and provide data support for satellite-based detection of phytoplankton community composition.关键词：Phytoplankton pigments;absorption coefficient;Coastal water;SeaWiFS;MODIS;Remote sensing dataset- 1145
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发布时间：2024-05-15
Data Articles
- “科技媒体新闻记者播报：地球观测组织GEO，作为地球观测领域的领军国际组织，自2005年成立以来，中国一直积极参与并担任联合主席国，为发展中国家和亚洲大洋洲区域发声。在过去的18年里，GEO不断推动全球地球观测的发展与合作。中国在其中发挥了重要作用，不仅在国内、亚洲大洋洲，还在国际层面取得了显著成果。为了进一步深化全球治理和融入全球创新网络，中国已经制定了未来的工作内容体系。这一体系从治理机制、项目合作、公共产品和人才队伍四个方面入手，旨在构建全球科技共同体，推动地球观测领域的持续进步。这一战略不仅展示了中国在地球观测领域的决心，也为全球科技合作树立了典范。”
摘要：The Group on Earth Observations (GEO) is the largest and most influential intergovernmental organization in the field of Earth observation. China is one of the founding members of GEO and has served as a co-chair for representing developing countries and the Asia-Pacific region since GEO’s establishment in 2005. This paper offers an in-depth analysis of GEO’s evolution and practices of the GEO over the past eighteen years. It discusses China’s related achievements at three levels: domestic, Asia-Pacific, and international. Furthermore, with a focus on governance mechanisms, project cooperation, public goods, and talent cultivation, the paper outlines China’s future engagement in global governance within GEO, aiming to accelerate integration into the global innovation network and collaborate in building a global community of science and technology.关键词：Group on Earth Observations;Global Earth Observation System of Systems;data sharing;knowledge service;earth intelligence;public goods;capacity building;global governance;a human community with a shared future- 65
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发布时间：2024-05-15 - “首届全国海岸带遥感大会在宁波圆满落幕。为期三天的会议以“遥感与海岸带可持续发展”为主题，集结了400多位专家学者，共襄盛举。会议围绕全球变化、海岸带生态环境、陆海统筹、海岸带灾害恢复、数字孪生与人工智能等关键议题展开深入探讨。院士们的精彩报告和177个分会场口头报告，共同梳理了海岸带遥感领域的发展脉络与未来方向。此次大会不仅促进了国内外专家学者的深入交流，更为中国海岸带遥感技术的进一步发展，以及海岸带可持续发展目标的实现，注入了新的活力与机遇。”摘要：The First National Conference on Remote Sensing of Coastal Zones was successfully conducted from October 25 to 27, 2023, in Ningbo, China. With the theme “Remote Sensing and Sustainable Development of Coastal Zones,” the conference featured a main venue and 14 thematic sessions, including 13 invited presentations and 177 oral reports. Over 400 experts participated in discussions covering topics such as global change and coastal zone ecology, land-sea integration, coastal zone urban-rural development, coastal zone remote sensing big data and decision-making services, coastal zone disaster and recovery of remote sensing, coastal zone digital twin, and artificial intelligence, as well as coastal zone stereoscopic observation and fine-scale remote sensing.The conference successfully summarized the current development status, challenges, and future directions in the field of remote sensing in coastal zones. By providing a platform for experts and scholars to engage in extensive and in-depth discussions, the event contributed to the further advancement of remote sensing in coastal zones in China, ultimately supporting the realization of sustainable development goals for coastal areas.关键词：remote sensing;coastal zones;sustainable development;ecology;artificial intelligence;expert opinion
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发布时间：2024-05-15
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