Outstanding Remote Sensing Young Research Paper
Theme Keywords： deep learninghyperspectral remote sensingsynthetic aperture radarsubpixel interpretationspatiotemporal characteristicsspatial symbiosis knowledge constrainttime series monitoringurban agglomeration
《遥感学报》依托品牌会议“遥感青年科学家研讨会”从前年发表的论文中，综合考虑论文的学术质量、创新水平、学术影响力、引用情况、阅读量、下载量等多个维度，最终遴选出10篇“卓越遥感青年论文”。
- The Paper
- Abstract：The analysis of the spatial and temporal changes of urban agglomeration construction land expansion is highly significant for optimizing the spatial pattern of urban agglomerations and promoting regional coordinated development.In this study, 19 urban agglomerations in China are selected as the research object. Based on remote sensing image data of medium-scale resolution in China from 1990 to 2020, the boundary vector of urban concentrated construction areas is extracted from the actual construction of a city by using the method of human-computer interaction. Results are compared with those of other related studies, and research is conducted from the aspects of urban agglomeration expansion process, development stage, expansion mode, and center of gravity migration law.The results show the following. (1) Compared with other research results, the urban boundary of this study exhibits higher accuracy and reliability. It is also closer to the real situation of a city. (2) In the past 30 years, the urban scale change curve of 19 urban agglomerations in China generally presents an “S” type, and the expansion process of urban agglomerations can be divided into three periods: slow, rapid, and stable expansion. Eastern urban agglomerations entered the rapid expansion period about 10 years earlier than western urban agglomerations. Most urban agglomerations entered a period of steady expansion after 2015. (3) By 2020, 19 urban agglomerations were in a high-level development stage, and the coordination degree of spatial expansion between internal central and peripheral cities was continuously improved. From the perspective of spatial distribution, 70% of the urban agglomerations in the eastern region are in the stage of decentralization, 56% of the urban agglomerations in the western region are in the stage of agglomeration attenuation, and eastern urban agglomerations are generally in a higher level of urban agglomeration development stage. (4) Based on the expansion scale of urban agglomerations in different directions, the spatial expansion modes of 19 urban agglomerations in China can be divided into circular, fan-shaped, and axial expansions. The spatial expansion mode is closely related to the number, location, and influence of core cities within an urban agglomeration. In addition, topographic conditions pose certain restrictions on the expansion direction of urban agglomerations. (5) The gravity center migration trajectories of urban agglomerations are different, and expansion direction may be affected by various factors, such as the location of core cities, the construction of new areas, traffic conditions, and terrain conditions. From the perspective of change in the center of gravity of urban agglomerations, the center of gravity of 84% of urban agglomerations was relatively stable in the past 30 years, i.e., basically located in the core city or the same city adjacent to the core city. The core city of urban agglomerations is attractive, and the expansion of the construction scale of a peripheral city is relatively average.The results of this study provide intuitive and accurate data for the study of urban expansion in China. Long-term and high-precision monitoring of urban construction scale can fully reflect the development process of urban agglomerations.Keywords：urban agglomeration;expansion process;expansion mode;spatiotemporal characteristics3825|8038|4
<HTML><L-PDF><Enhanced-PDF><Meta-XML>Updated：2024-12-30 - Abstract：Accurate monitoring of Soil Organic Carbon Density (SOCD) is important for regulating soil carbon sinks and rationally using soil resources. Airborne hyperspectral images provide important data sources for SOCD mapping. The noise in the spectrum affects the accuracy of SOCD estimation because airborne hyperspectral images are easily affected by external factors during data collection. A set of technical processes that are suitable for airborne hyperspectral data processing is still lacking. Therefore, this study aims to investigate the technical process of SOCD estimation based on airborne hyperspectral images. The original spectra are preprocessed by First Derivative (FD) and Continuum Removal (CR) transform. Genetic Algorithm (GA) was used to select the feature bands. Different regression methods, such as Partial Least-Squares Regression (PLSR), Multiple Linear Regression (MLR), Support Vector Machine (SVM), and Artificial Neural Network (ANN), were used to estimate SOCD. Results showed that the accuracy of SOCD prediction for original, FD, and CR spectra was improved after feature band selection by GA. With the feature bands of original spectra, the R² of SOCD predicted by PLSR, MLR, SVM, and ANN are 0.672, 0.621, 0.551, and 0.678, respectively. The range of R² are 0.452—0.593 and 0.332—0.602 with FD and CR feature bands, respectively, which demonstrate large errors. The feature bands of the original spectrum were used in this study for SOCD mapping. The SOCD predicted by four regression models has a highly similar trend in space and is similar to the SOCD measured value. The points with large absolute errors mostly occur near the edges of the sampling points.Keywords：soil organic carbon density;airborne hyperspectral images;genetic algorithm;digital soil mapping1865|2883|6<HTML><L-PDF><Enhanced-PDF><Meta-XML>Updated：2024-02-29
- Abstract：With the rapid development of spatial technology, the resolution of remote sensing images gradually improves. The detailed information and spatial information contained in remote-sensing images are also richer. The ensuing problems are that the difference between various categories becomes and the difference between the same categories becomes larger, i.e., the phenomenon of the same spectrum of foreign objects and the different spectrum of the same objects is serious. However, the existing dual-modal segmentation methods do not extract the dual-modal feature information of remote-sensing images separately, and the fusion features are insufficient. The details of upsampling recovery are also insufficient, resulting in the inability to accurately and efficiently learn remote-sensing image information, thereby resulting in segmentation errors, edge blur, and other problems.This study proposes a high resolution remote-sensing image segmentation based on dual-modal efficient feature learning. The algorithm designs appropriate encoders for different modal remote sensing images, efficiently extracts dual-modal features, and reduces the differences between different path features through interactive reinforcement modules. Then, the dual-modal feature aggregation module and the deep feature-extraction module are proposed to further fuse and extract the dual-modal features. As a result, the network can fully learn the complementary information of the dual-modal. Finally, a multi-layer feature upsampling module is proposed, which uses high-level features with rich semantic information to weight the low-level features with rich detail information. Gradual upsampling is then conducted to achieve efficient feature recovery and improve segmentation performance.In this paper, experiments on the Potsdam and Vaihingen datasets demonstrate that the overall accuracy reaches 94.52% and 90.45%, respectively. Experimental results show that the segmentation effect of the proposed algorithm is better than that of existing algorithms. The proposed algorithm can efficiently extract and fuse the multi-modal complementary features of high resolution remote-sensing images and improve the segmentation accuracy of remote-sensing images.This study proposes a high-resolution remote-sensing image segmentation based on dual-modal efficient feature learning. Experiments on the ISPRS Potsdam and Vaihingen datasets show that the proposed model is more suitable for segmenting low vegetation and trees, buildings, and roads with very similar spectral features. It can also achieve the accurate segmentation of small targets, such as cars. However, the complexity of the model needs to be further reduced, and much room for improvement in accuracy remains. In the future, a better segmentation network will be designed to fuse more than two modal features and thus obtain more feature information to achieve more accurate remote sensing image segmentation.Keywords：remote sensing image segmentation;efficient feature extraction;integration;dual-modal feature aggregation;deep feature extraction;multilayer feature upsampling1140|3483|2<HTML><L-PDF><Enhanced-PDF><Meta-XML>Updated：2024-03-22
- Abstract：Knowledge and data are the two main elements that have characterized the development of remote sensing image interpretation for decades. With the continuous enrichment of sensor platforms and rapid breakthroughs in deep learning, big data, multi-modal, and long time-series methodologies, data-driven intelligent remote sensing image interpretation has become a hot research direction in recent years. However, in the deepening and expanding research and applications, the limitations of data-driven methods such as difficult reuse between different scenarios, strong training sample dependence, and weak interpretability are beginning to emerge. Various types of knowledge accumulated in the long-term remote sensing image interpretation practice have the characteristics of objective reality, certainty, scene adaptability, interpretability, etc., which can be complemented with data-driven approaches, and the dual-driven of knowledge and data is becoming a new direction of remote sensing image interpretation. This paper first reviews the major stages in the development of remote sensing image interpretation and the respective roles of knowledge and data in each of these stages. Then the main types of knowledge involved in remote sensing image interpretation are summarized and categorized into fourteen types. The fusion of knowledge and deep learning is an important path to achieve the dual-drive of knowledge and data, and this paper summarizes five categories and fifteen subcategories of knowledge and deep neural network fusion methods with relevant cases. From the perspective of knowledge types, this paper further provides an overview of existing applications of remote sensing interpretation with joint knowledge and data. The effectiveness and capability increment of fusing knowledge and data is demonstrated by the analyses of typical examples. Lastly, this paper gives a systematic prospect on the framework and key techniques for knowledge and data compound driven remote sensing image interpretation.Keywords：remote sensing image interpretation;knowledge driven;data driven;artificial intelligence;knowledge graphs;deep learning;natural resources;review12415|19295|11<HTML><L-PDF><Enhanced-PDF><Meta-XML>Updated：2024-12-30
- Abstract：Flood disasters are a great threat to the national economy and people’s property along the lakes and rivers in China. Synthetic Aperture Radar (SAR) adopts active imaging methods that can realize all-weather imaging and ensure continuous observation of flood disaster areas under severe weather, such as heavy rains and clouds. The current flood-monitoring methods based on SAR images often have problems, such as difficulty in threshold selection, high computational cost, or inefficient use of time-series information. Aiming at the above problems, this paper makes full use of information from time-series SAR image sequence to design an effective and stable method of monitoring flood, which can be adapted to complex areas.Through preprocessing and statistical analysis of the image sequence, two normalized difference indices including submerged range extraction index and submerged range in vegetation area extraction index are designed and applied to calculate the candidate area of flood inundation. Then, the adaptive selection method of threshold for flood extraction is given based on the stability assumption of vegetation seasonal distribution in the same area. Finally, considering the characteristics of the surrounding features of the lakes in China, a post-processing process is designed. The process involves removing spots and holes, excluding areas with large slopes and filtering out fragmented areas with large rectangular degrees. Post-processing is conducted to optimize the extraction area for the final results of flood-inundation range.In the experiment, this paper takes the East Dongting Lake basin as the main research area to verify the effectiveness of the proposed method by comparing the extraction accuracy with the other four methods. Experimental results prove that the overall extraction accuracy of the proposed method is higher than that of all comparative methods. To achieve the purpose of flood-disaster monitoring and evaluation, an analysis of the flood-disaster situation in the East Dongting Lake Basin in 2020 and an analysis of flood submerged land cover types are conducted. The method has also been successfully applied to the data of the East Dongting Lake basin in previous years and the Poyang Lake basin in that the method can be applied across time and space.Based on the time-series SAR image sequence, this paper proposes an effective method, forms a detailed process flow, and constructs a general framework for flood monitoring. The proposed method has advantages of simple parameter setting and low user dependence on threshold determination. Experiments show that the method has high extraction accuracy of submerged areas with good robustness and versatility. The proposed method can be applied to different flood-monitoring scenarios across time and space and can preliminarily distinguish different attributes of submerged areas. Thus, a certain reference is provided for flood-disaster monitoring, assessment, and early warning in other regions.Keywords：Flood disaster;synthetic aperture radar;time series monitoring;hydrological remote sensing;SAR;Dongting Lake5754|5375|11<HTML><L-PDF><Enhanced-PDF><Meta-XML>Updated：2024-03-22
- Abstract：Although the Deep Semantic Segmentation Network (DSSN) has notably enhanced remote-sensing image semantic segmentation, it still falls short of human experts’ visual interpretation. Unlike DSSN’s data-driven, pixel-level optimization, human experts rely on visual features, semantic insight, and prior knowledge for remote-sensing image interpretation. DSSN’s pixel-level approach is constrained by spatial scale, lacking comprehensive target inference and struggling to bridge structured data and unstructured knowledge. In response to the two issues above, this paper proposes a geographic knowledge graph-guided deep semantic segmentation network for remote-sensing imagery. We use the ground-object semantic information and geoscience prior knowledge extracted from the geographic knowledge graph to construct loss constraints, thereby autonomously guiding the training process of DSSN.The essence of our approach lies in the intricately crafted design of loss constraints. These loss constraints include the entity-level connectivity constraint and the inter-entity symbiosis constraint. The former calculates the loss in the unit of connected domain entities instead of pixels to achieve overall constraints on the entity. The latter embeds the spatial symbiosis knowledge quantified by the symbiosis conditional probability into the data-driven DSSN to constrain the spatial distribution of segmented entities. The entity-level connectivity constraint guides DSSN to autonomously learn entity-level feature representations during training. Accordingly, the segmentation results become more holistic and suppresses blurry boundaries and random noise. The inter-entity symbiosis constraint adjusts the spatial distribution of entities according to the spatial semantic information and the prior geoscience knowledge. This adjustment realizes the automatic optimization of the spatial distribution of segmented entities.Extensive experiments show that under the guidance of the entity-level connectivity constraint and the inter-entity symbiosis constraint, DSSN can complete the learning of entity-level features. It can also automatically optimize the spatial distribution of ground objects based on spatial symbiosis knowledge, thereby effectively improving the performance of remote-sensing image semantic segmentation.Our novel geographic knowledge graph-guided approach to deep semantic segmentation in remote-sensing imagery has successfully addressed the challenges posed by DSSN’s pixel-level optimization. By incorporating entity-level connectivity and inter-entity symbiosis constraints, we have enabled DSSN to autonomously learn comprehensive feature representations and optimize spatial distribution. The resulting improvements in semantic segmentation performance showcase the potential of merging domain-specific knowledge with data-driven techniques, bridging the gap between automated methods and human interpretation in remote-sensing image analysis.Keywords：Geographic knowledge graph;deep semantic segmentation network;entity-level connectivity constraint;spatial symbiosis knowledge constraint;geographic knowledge embedding optimization2033|5443|15<HTML><L-PDF><Enhanced-PDF><Meta-XML>Updated：2024-03-22
- Abstract：Building change detection is essential to many applications, such as monitoring of urban areas, land use management, and illegal building detection. It has been seen as an effective means to detect building changes from remote-sensing images.This paper proposes an object-based Siamese neural network, labeled as Obj-SiamNet, to detect building changes from high-resolution remote-sensing images. We combine the advantages of object-based image analysis methods and Siamese neural networks to improve the geometric accuracies of detected boundaries. Moreover, we implement the Obj-SiamNet at multiple segmentation levels and automatically construct a set of fuzzy measures to fuse the obtained results at multi-levels. Furthermore, we use generative adversarial methods to generate target-like training samples from publicly available datasets and construct a relatively sufficient training dataset for the Obj-SiamNet model. Finally, we apply the proposed method into three high-resolution remote-sensing datasets, i.e., a GF-2 image-pair in Fuzhou City, and a GF2 image pair in Pucheng County, and a GF-2—GF-7 image pair in Quanzhou City. We also compare the proposed method with three other existing ones, namely, STANet, ChangeNet, and Siam-NestedUNet.Experimental results show that the proposed method performs better than the other three in terms of detection accuracy. (1) Compared with the detection results from single-scale segmentation, the detection results from multi-scale increases the recall rate by up to 32%, the F1-Score increases by up to 25%, and the Global Total Classification error (GTC) decreases by up to 7%. (2) When the number of available samples is limited, the adopted Generative Adversarial Network (GAN) is able to generate effective target-like samples for diverting samples. Compared with the detection without using GAN-generated samples, the proposed detection increases the recall rate by up to 16%, increases the F1-Score by up to 14%, and decreases GTC by 9%. (3) Compared with other change-detection methods, the proposed method improves the detection accuracies significantly, i.e., the F1-Score increases by up to 23%, and GTC decreases by up to 9%. Moreover, the boundaries of the detected changes by the proposed method have a high consistency with that of ground truth.We conclude that the proposed Obj-SiamNet method has a high potential for building change detection from high-resolution remote-sensing images.Keywords：change detection of remote sensing;Siamese Neural Network;object-based multi-scale analysis;fuzzy sets fusion;Generative Adversarial Network;Very High Resolution remote sensing images2866|2851|8<HTML><L-PDF><Enhanced-PDF><Meta-XML>Updated：2024-03-22
- Abstract：The applications of remote sensing images in numerous fields have been increasing with the continuous development of aerospace and remote sensing technologies. HyperSpectral Image (HSI) is a common type of remote sensing image that comprises a series of two-dimensional remote sensing images as a 3D data cube. Each two-dimensional image in HSI can reveal the reflection/radiation intensity of different wavelengths of electromagnetic waves, and each pixel of HSI corresponds to the spectral curve reflecting the spectral information in different wavelengths. Therefore, the hyperspectral remote sensing images are characterized by “spatial-spectral integration,” which contains not only spectral information with strong discriminant but also rich spatial information. Therefore, the hyperspectral data have considerable application potential.Hyperspectral anomaly detection aims to detect pixels in a scene with different characteristics from surrounding pixels and determines them as anomalous targets without any previous knowledge of the target. Hyperspectral anomaly detection is an unsupervised process that does not require any priori information regarding the target to be measured in advance; thus, this type of detection plays a crucial role in real life. For example, anomaly target detection technology can be used to search and rescue people after a disaster, quickly determine the fire point of a forest fire, and search mineral points in mineral resource exploration. Hyperspectral anomaly detection has been a popular research direction in the area of remote sensing image processing in recent years, and a numerous researchers have conducted extensive research and achieved rich research results.However, hyperspectral anomaly detection still encounters many difficult problems. For example, the targets of the same material may exhibit various spectral characteristics due to the different imaging equipment and environment, which may interfere with the detection results and lead to the problem of “same object with different spectra.” Meanwhile, the targets of different materials may also exhibit the problem of “different objects with different spectra.” Then, most of the existing hyperspectral anomaly detection algorithms are only in the laboratory stage and with low technology maturity. Furthermore, the hyperspectral data may have numerous spectral bands that contain a considerable amount of redundant information, which increases the difficulty of data processing. Moreover, the number of publicly available hyperspectral anomaly detection datasets is insufficient and mostly old.In this paper, the main research progress of hyperspectral anomaly detection is first summarized. The existing mainstream algorithms are then classified and summarized. These algorithms are mainly divided into five categories: statistics-based anomaly detection methods, data expression-based anomaly detection methods, data decomposition-based anomaly detection methods, deep learning-based anomaly detection methods, and other methods. Through the investigation, analysis, and summary of the existing methods, three future development directions of hyperspectral anomaly detection are proposed. (1) Database expansion: new datasets with additional images and highly sophisticated remote sensing sensors are introduced. (2) Multisource data combination: the advantages of different imaging sensors and various types of remote sensing data are maximized. (3) Algorithm practicality: the anomaly detection algorithms are relayed for application on real platforms.Keywords：remote sensing;hyperspectral remote sensing;hyperspectral anomaly detection;deep learning;matrix factorization3734|7181|4<HTML><L-PDF><Enhanced-PDF><Meta-XML>Updated：2024-02-29
- Abstract：Hyperspectral remote sensing technology has been widely used in remote sensing, agriculture, geological exploration, and other fields, and hyperspectral image classification is one of the most important research directions. Benefiting from sufficient label information, supervised learning has achieved good results in this field. However, in many practical applications of hyperspectral remote sensing images, sufficient label samples are difficult to obtain. One of the most important reasons is the widespread use of hyperspectral remote sensing technology, which produces huge amounts of unlabeled data. Another is the high cost of labeling. Meanwhile, unsupervised learning cannot accurately cluster unknown data, and its clustering categories are to match to real categories. Both supervised and unsupervised learning have their unavoidable disadvantages. Therefore, semi-supervised learning that uses a large number of unlabeled samples and a small number of labeled samples should be explored. In recent years, significant progress has been made in the semi supervised classification of hyperspectral remote sensing images. Researchers have proposed many innovative algorithms and technologies to address the problem of insufficient data annotation. This article reviews the progress of the semi supervised classification research on hyperspectral remote sensing images in recent years, discussing key technologies and methods.This paper starts with semi-supervised classification and hyperspectral remote sensing technologies. First, the first part of this paper introduces some basic concepts of semi-supervised learning, including semi-supervised and unsupervised learning, supervised learning, and the application of semi-supervised learning. The second part introduces the development of hyperspectral remote sensing imaging technology domestically and internationally and the application of hyperspectral remote sensing in various fields, such as land and resource surveys, agriculture and forestry remote sensing, and urban environmental monitoring. Second, the three basic assumptions of the theory, process, and data distribution of semi-supervised learning are analyzed, and four typical types are introduced: low-density separation, generative, disagreement-based (difference-based), and graph-based methods. The algorithm flow and core ideas of each method are introduced in detail. The summarized current development status, typical algorithms, and research progress of hyperspectral remote sensing image classification are analyzed. Further, the advantages and disadvantages of each algorithm are enumerated. Then, common open-source algorithms were compared on three publicly available datasets, namely, Indian Pines, Pavia University, and Houston 2013. Finally, by analyzing existing semi-supervised learning technologies and experimental results, the challenging problems and development trends of semi-supervised learning in hyperspectral remote sensing are summarized.The graph-based semi-supervised classification method performs better than other semi-supervised classification methods, which may be because the graph model can model the relationship and similarity between samples, connect similar samples, and capture the intrinsic structure and similarity in a dataset.Semi-supervised learning can efficiently utilize both labeled data and unlabeled data. The future development trend of semi-supervised classification is mainly in three aspects: how to effectively use a large number of unlabeled samples; how to fully consider multiple factors, such as performance and computational complexity; and how to select features. These aspects will affect the stability, generalization, practicability, and performance of the algorithm.Keywords：hyperspectral image;semi-supervised classification;Low-density separation;generative model;graph neural network2157|7614|9<HTML><L-PDF><Enhanced-PDF><Meta-XML>Updated：2024-02-29
- Abstract：Hyperspectral remote sensing is an advanced technique for earth observation that combines physical imagery and spectral analysis technology. Therefore, hyperspectral remote sensing can obtain fine spectral and rich spatial information from imaged scenes, merging the spatial and spectral information into data cubes. These data cubes exhibit narrow spectral bands and a high spectral resolution, allowing different land cover objects to be distinguished. Hyperspectral remote sensing images, with their high spectral resolution and cube characteristics, have gradually become among the most essential supporting data in remote sensing engineering applications. However, due to spatial resolution limitations, the mixed pixel problem has hindered the development of hyperspectral remote sensing in fine-scale object information extraction. At present, hyperspectral unmixing is one of the most effective analytical techniques for dealing with mixed pixel problems, aiming to break through spatial resolution limitations by analyzing the components within pixels. Hyperspectral unmixing refers to any process that separates pixel spectra from a hyperspectral image into a collection of pure constituent spectra, called endmembers, and a set of corresponding abundance fractions. At each pixel, the endmembers are generally assumed to represent the pure materials in the scene, while the abundances represent the percentage of each endmember. For the fine-scale interpretation of object information, many unmixing methods have been developed for hyperspectral remote sensing images in the remote sensing field over the past 30 years, mitigating the impact of mixed pixel problems on quantitative remote sensing analysis. Currently, with the development of deep learning, an increasing number of deep learning theories and tools are used to deal with mixed pixel problems. Many new methods using deep learning for unmixing have been developed, and unmixing technology research has gradually entered a new stage of development with deep learning. Deep-learning-based methods make better use of hidden information, have a relatively lower dependence on prior knowledge, and have a stronger adaptability to complex scenes than traditional unmixing methods. Although deep learning-based unmixing methods have developed rapidly in recent years and are diverse, the analysis and summary of the work on such methods have not kept up with the pace of technological development. A timely summary of the latest research progress on developing a specific field of research has a significant role in promoting the technology. Thus, this paper sorts out the existing deep learning-based unmixing methods, classifying them according to the adopted spectral mixing models, the deep network training modes, and whether spectral variability is considered. Furthermore, this paper introduces these deep learning-based approaches and summarizes their characteristics, making the use of these methods in special works convenient for users or readers. Finally, the development of deep learning methods is summarized, referring to the current technical status, characteristics, and development prospects. In addition, some existing deep learning unmixing methods were tested in this study and organized to facilitate the research and application of unmixing technology. The development of deep learning will continue to promote the progress of unmixing techniques. In recent years, deep learning-based unmixing methods have developed rapidly and have been gradually used in vegetation distribution investigation and agricultural yield estimation, implying their good development prospect and application value. his paper can provide valuable references for researching unmixing technology in the future.Keywords：hyperspectral remote sensing;unmixing;deep learning;machine learning;deep neural network;remote sensing image processing;remote sensing intelligent interpretation;subpixel interpretation7349|8760|9<HTML><L-PDF><Enhanced-PDF><Meta-XML>Updated：2024-02-29
