最新刊期
卷 23 ， 期 4 ， 2019
- 摘要：Remote sensing as a science and technology needs further and deeper applications. Remote sensing is already ubiquitous. Remote sensing researchers should not stay within the processing and analysis of traditional aerial and space-borne remotely sensed data, instead, they should take advantage of the emergence of ground video, recording, even smell detectors, as well as socio-economic big data in their studies. Only if remote sensing scholars make a stride outside of their comfort zone and carry out cross-disciplinary studies, will there be greater scientific returns. At the end, I state that remote sensing can make a contribution towards the realization of every Unite Nations sustainable development goals in a hope for it to be of help to young remote sensing scholars.关键词：remote sensing science;wireless sensor networks;remote sensing application;sustainable development goals
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发布时间：2021-06-07 - 摘要：Diurnal Surface Temperature Cycle (DSTC) model is an important input parameter in the field of meteorology, hydrology, and ecology. In the past 20 years, various DSTC models under clear-sky conditions have been developed on the basis of Spinning Enhanced Visible and Infrared Imager (SEVIRI) and Geostationary Operational Environment Satellite (GOES) satellites. However, only a few related studies have focused on China due to its complex topography, geomorphology, and climatic conditions. This situation restricts the application and development of DSTC models in the region. Although the DSTC model is mature, its evaluation is often based on the data of a certain point or day, which lacks verification at a large range of time and spatial scales. Moreover, the free variable setting of the DSTC model cannot meet the need at large time and spatial scales. Thus, the variable setting of the DSTC model must be expanded. In this study, six DSTC models were evaluated on the basis of FY-2F land surface temperature product at the monthly average and large space scales for China. In addition, JNG06 model was used to analyze the diurnal variation characteristics of Land Surface Temperature (LST) with the changes in seasonality, latitude and longitude, and land cover types. Semi-empirical model is simple and convenient and has a wide range of applications. Physical model is close to the actual physical condition of the surface. Therefore, five semi-empirical models, namely, GOT01, VAN06, JNG06, INA08, and GOT09, and the GEM-V physical model were used for DSTC simulation and analysis. On the basis of the geographical location of 194 meteorological stations, the corresponding FY-2F LST data were extracted, and the average monthly and hourly LST data after quality control were set as the model initial value. To evaluate the fitting accuracy of DSTC model in China, the model evaluation was divided into two parts: 1) the fitting of DSTC model was divided into five time periods, and the precision of DSTC model was analyzed; and 2) the fitting accuracy of each model under different land cover types was statistically explored on the basis of the land cover types of 194 stations. JNG06 model was also used to analyze the diurnal variation characteristics of LST with the changes in seasonality, latitude and longitude, and land cover types. Analysis of the average root mean square error (RMSE) of five time windows showed that GOT09 model obtained the global optimal fitting accuracy with an RMSE of 0.89 K, followed by JNG06 and GEM_V models with RMSEs of 0.92 and 0.94 K, respectively. GOT01, INA08, and VAN06 models obtained the worst accuracy. Each model had the best fitting precision in urban and built-up, cropland/natural vegetation mosaic, and evergreen broadleaf forest with an RMSE between 0.89 and 0.92 K. The fitting precision in mixed forest and cropland followed with an RMSE of around 1.0 K. Each model had the worst fitting precision in barren or sparse vegetation and savannas with an RMSE of above 1.3 K. From the results, we can conclude that the accuracies of GOT01, VAN06, and INA08 are poor in all land cover types and time windows, and the remaining models are relatively robust with close accuracy. The land surface temperature simulated by JNG06 model in four land cover types vary with the changes in latitude and longitude. The rule of LST varies with the change in latitude and but is unaffected by the change in longitude. The DSTC model can be used as the input parameter of climate and hydrological models. The model can also be used as reference for future studies on the DSTC model and its applications.关键词：remote sensing;FY-2F;MCD12C1;LST (Land Surface Temperature);diurnal surface temperature cycle;meteorology stations
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发布时间：2021-06-07 -
摘要：The coastline refers to the boundary line between land and sea. The coastline detection is an important part of studying the effects of land-sea interaction and human activities on coastal zones. It is also significant for the effective development, integrated management and sustainable exploitation of coastal resources and protection of coastal ecosystems. Therefore, it is critical to achieve the extraction of coastline quickly and accurately. Remote sensing technology has gradually become a way to detect the location of the coastline and monitor its dynamic changes with its wide range, high temporal resolution, high spatial resolution, multispectral, low cost and other prominent advantages, overcoming the shortcomings of traditional coastline detection methods of long time period, high intensity of labor and so on. This paper reviews the research progress on methods of automatic coastline extraction based on remote sensing images proposed in recent years. First, the definition and classification of the coastline are given. The two stages of extraction of the instantaneous waterline and the real coastline and the specific extraction process are clarified. Then, these methods are divided into eight categories: threshold segmentation, edge detection operator, active contour model, data mining, multiresolution analysis, object-oriented, polarization and other methods. And the basic ideas of the main methods of coastline extraction are elaborated. Finally, the advantages and disadvantages of all kinds of methods are analyzed and compared. The principle of tidal correction and the methods of accuracy evaluation are expounded, and the research work of the next step is prospected. The detailed feature description of main methods are as follows. Threshold segmentation method is simple and easy to implement. While the selection of appropriate threshold is a certain degree of difficulty, and the extraction accuracy needs to be improved. The edge detection operator method has a good effect on the extraction of edge. Simultaneously, this method is susceptible to noise and prone to detect pseudo edges, even the continuity of extracted coastline is poor. The extraction result of the active contour model is accurate. However, the model has high complexity and large computational burden. Data mining method utilizes intelligent means to achieve automatic extraction of coastline. But a variety of methods need to be combined to make the extraction accuracy higher. Although the multiresolution analysis method can obtain rich edge information, the mostly applied wavelets have a limitation of dealing with directional information. Object-oriented approach can achieve image segmentation of a higher level and reduce the influence of texture and other characteristics inside the cell. It cannot make full use of the implicit information of the image when the amount of data is large. And the polarization method is essentially a threshold segmentation method. The only difference is the selection method of threshold. The polarization method achieves threshold selection by usage of different polarization methods of SAR images. In the end, in view of the deficiency of currently existing methods, the following feasible research approaches and prominent research prospects of automatic coastline extraction based on remote sensing images are put forward: constructing dataset, utilizing hyperspectral data, employing deep learning method, adopting swarm intelligence algorithm to optimize the parameters in the method of active contour model, using multiscale transform which contains directional information to improve the extraction accuracy of multiresolution analysis method, combining various kinds of methods to synthesize their advantages and achieving the automatic extraction of coastline with subpixel precision.关键词：remote sensing;coastline;remote sensing image;automatic extraction;waterline;accuracy evaluation- 834
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发布时间：2021-06-07 -
摘要：Remote sensing applications have been promoting the development of satellite sensors and their performances. The satellite sensors are now possessing higher and higher data resolutions, and it in turn greatly facilitating remote sensing applications. The improvement of spatial resolution makes it possible to precisely record textures and spatial features of land covers. The improvement of spectral resolution makes it possible to precisely classify and retrieve parameters. The improvement of temporal resolution makes it easier to record the temporal changes of land covers during different phases. However, due to the limitation of sensors, satellite data can not possess high spatial resolution, high temporal resolution and high spectral resolution at the same time, which greatly hinders further remote sensing applications. Remote sensing data fusion is one of the effective solutions to deal with the problem of limitation in sensors’ resolutions, which is to integrate data from different sources and with different resolutions using algorithm methods to get richer information than one single image data. In recent years, the remote sensing data fusion methods have been greatly developed and related articles boost in growing numbers and importance. Thus, this article aims to systematically introduce the remote sensing data fusion and its current progress. Former reviews of remote sensing data fusion divides fusion methods into different categories. Based on processing levels, the remote sensing data fusion methods can be divided into pixel-level fusion, feature-level fusion and decision-level fusion. And based on the data sources, the remote sensing data fusion methods can be divided into homogeneous data fusion, heterogeneous data fusion, fusion for remote sensing observation and station data, and fusion for remote sensing observation and non-observed data. And this article adopts another category system which makes it more systematic and comprehensive in mathematical principles. In order to systematically indicate the current progress and developed history of remote sensing data fusion methods, this article first did bibliometric analysis on the remote sensing data fusion articles from data sources from Web of Science (WOS) and Chinese National Knowledge Infrastructure (CNKI). The data was preprocessed and has removed duplicates. Analysis including the aspects of yearly publishments, countries and organizations, journals and key words to see the history and trends have been done by Histcite, Bibliometric, Citespace and Endnote. Then the remote sensing fusion methods are systematically introduced. According to different resolution improvements, this article firstly divided the fusion methods into three categories: fusion methods emphasizing improvements of spatial resolution development, spectral development and temporal development, and based on that we further divide them into subcategories according to different mathematical principles. And the basic principles, some of the important methods, the advantages and flaws of these methods are systematically introduced in this section. Also, we did detailed instructions on fusion result assessment methods including methods with reference image and without reference image. Under these two categories we further introduced different assessment metrics and its applicable scenarios. And finally, this article made summaries of these fusion methods and described its features and directions. And finally makes predictions of future trends in remote sensing data fusion methods.关键词：remote sensing data;data fusion;image fusion;fusion quality assessment;document analysis- 1327
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发布时间：2021-06-07 -
摘要：Surface albedo qualifies the proportion of incoming light reflected by the land surface and plays an important role in the earth’s energy budget. In the WATER experiment of Heihe in 2008, we developed an algorithm for estimating the albedo of Wide-angle Infrared Dual-mode line/area Array Scanner (WIDAS) based on the MODIS Bidirectional Reflection Distribution Function (BRDF) archetype-based algorithm for the narrow-angle observations of the airborne WIDAS. However, in the HiWATER experiment in 2012, the WIDAS observation angle range was upgraded from the early 30° to the maximum observation zenith angle of 52°, which impacted the data preprocessing (radiation scaling, atmospheric correction, and multi-angle registration). This condition caused significant noise and created new challenges for the surface albedo inversion of WIDAS. In the current study, we addressed new problems and adopted new surface observation data to verify the ability of the BRDF archetype-based algorithm to retrieve the albedo of WIDAS. To obtain quasi-real-time BRDF prior knowledge, we first extracted five BRDF archetypes as a priori information from 500 m MODIS BRDF parameter product (MCD43A1) within the Heihe experimental region. Then, we applied these BRDF archetypes to airborne WIDAS multi-angular observations for albedo estimates based on the hotspot-corrected linear kernel-driven BRDF model, that is, RossThickChen-LiSparseReciprocal model. Finally, field albedo measurements were conducted to validate the broadband albedo estimates. We compared three commonly used albedo estimate methods, namely, the BRDF archetype-based algorithm that was developed in our previous paper; full-inversion BRDF/albedo algorithm that has been adopted as the operational MODIS BRDF/albedo algorithm; and Lambertian assumption method that is commonly used to estimate surface albedo, especially when only nadir observations are available. The performance of the BRDF archetype-based algorithm was verified by comparison and analysis of the algorithms. Unsurprisingly, the accuracy of the albedo retrievals by using the BRDF archetype-based algorithm was obtained at 0.034, which was 18% and 71% higher than those of the full inversion algorithm and the Lambertian assumption method, respectively. The inversion and verification of the WIDAS demonstrated that the BRDF archetype-based method was noise resistant and obtained stable albedo estimates. Therefore, our previous conclusions were confirmed by using new WIDAS observations. Given the merit of the proposed archetype-based algorithm, we strongly recommend it to the user community, especially for narrow-angle observations that need a priori information for stable retrievals of surface albedo.关键词：remote sensing;Heihe;MODIS;Wide-angle Infrared Dual-mode line/area Array Scanner (WIDAS);kernel-driven BRDF model;AFX;BRDF archetype;surface albedo- 566
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发布时间：2021-06-07 - 摘要：Abundance estimation (AE) plays an important role in the processing and analysis of hyperspectral images. Constrained linear regression is usually developed to estimate abundance matrix due to its simplicity and mathematical tractability. However, this approach only focuses on the fitness between the estimated and ground-truth data without considering the internal variability such as the similarity among the first-order gradients and among the second-order gradients. To improve the accuracy of the AE, a novel method of adding internal variability to sparse low-rank AE was proposed. First, first- and second-order gradient constraint terms were used to modify the traditional mathematical model of sparse and low-rank AE. Second, norm inequality and optimization theory were applied to demonstrate the validity of the novel model. The model has been proven applicable to other related fields under constraint conditions. Third, auxiliary variables were utilized to transform the mathematic model to the enhanced Lagrange function (ELF). Finally, the ELF was solved by the alternating direction method of multipliers to estimate the abundance of hyperspectral images. In general, the traditional method of sparse and low-rank AE is the alternating direction sparse and low-rank unmixing (ADSpLRU). In this study, ADSpLRU-FOG refers to the method that adds the first-order gradient to the sparse and low-rank AE, whereas ADSpLRU-FSOG refers to the method that adds first- and second-order gradients to the sparse and low-rank AE. Experiment carried on the USUG library showed that, (1) in the convergent experiment, ADSpLRU-FOG and ADSpLRU-FSOG algorithms converged to a slightly lower NMSE than ADSpLRU. ADSpLRU-FSOG algorithm converged to the lowest NMSE among the three methods. (2) In the robust experiment, ADSpLRU-FOG and ADSpLRU-FSOG algorithms reached higher estimation accuracy than ADSpLRU in terms of SRE under white and colored noises. Among them, ADSpLRU-FSOG achieved remarkably higher SRE value than the other methods. (3) In the visual experiment, ADSpLRU-FOG algorithm could maintain the first-order gradient structure of the data more than ADSpLRU. Meanwhile, the ADSpLRU-FSOG algorithm could preserve the second-order gradient structure of the data better than ADSpLRU-FOG and ADSpLRU algorithms. Experiment based on the Urban and Jasper actual hyperspectral database showed that the accuracy of abundance matrix estimation from ADSpLRU-FSOG was better than those from ADSpLRU and ADSpLRU-FOG. Experimental results suggest that the novel method of adding the internal variability to the abundance matrix estimation can improve convergent behavior, maintain the structure of information of first-order and second-order gradients, obtain comparable estimation accuracy, and enhance robust performance for AE.关键词：abundance estimation;CLR;internal variability;sparse and low-rank;ADMM
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发布时间：2021-06-07 - 摘要：Soil moisture content is an important parameter in hydrology, meteorology, and agriculture and is vital for meteorological forecast, flood disaster, and water resource cycle. Global positioning system interferometric reflectometry is a new remote sensing technique with low cost and high efficiency and resolution. This technique can be used to estimate near-surface soil moisture for the area surrounding the antenna from signal-to-noise ratio (SNR) data. In this study, a non-linear sliding estimation method of soil moisture based on multi-satellite fusion was proposed in consideration of the advantages of multi-satellite convergence and the time and space scale of soil moisture. First, the direct and reflection signals of GPS satellites were separated by means of low-order polynomial fitting. Then, the sinusoidal fitting model of reflection signals was established, and the relative delay phase of the SNR interferogram was obtained. Finally, a linear regression model was used to analyze and select the phase of SNR interferogram, and the sliding estimation method of the soil moisture using the least square support vector machine based on multi-system fusion was established. On the basis of the monitoring data provided by US Plate Boundary Observations Project, the feasibility and effectiveness of using single and multiple GPS satellites for sliding estimation of soil moisture were compared and analyzed. Results of the two types of data showed that the linear regression equation could efficiently describe the relationship between relative retardation phase and soil moisture and effectively select GPS satellites by setting the threshold range of correlation coefficient. The optimal parameters of least square support vector machine were selected by grid search method. Over-fitting did not occur in the process of multi-star fusion inversion, and the advantage of non-linear weight determination was fully exerted. When a single satellite was used for soil moisture inversion, the variation law of soil moisture was inaccurately obtained, and the error of inversion error fluctuated greatly, thereby resulting in the jump phenomenon. When the rolling multi-star fusion inversion model was used, the fluctuation of inversion error was relatively close, thereby effectively suppressing the transition phenomenon. The correlation coefficients between the estimated results and the measured values of soil moisture were 0.942 and 0.962, respectively. The root mean square errors were 0.072 and 0.032, which were at least 18.18% higher than those of some single satellites. The theoretical analysis and experiment showed that this method had fully utilized the advantages of least square support vector machine and effectively integrated the performance of each satellite. Overall, the method required less modeling data, the sliding mode could achieve long time estimation, and the estimation error was relatively stable. This method not only ensured the stability of the local error in the estimation process but also effectively restrained the abnormal jump phenomenon easily when single satellite was estimated. The inversion process was not easily affected by a single satellite. Therefore, the estimation of soil humidity can be treated as non-linear event, and multi-system fusion estimation is feasible and effective.关键词：remote sensing;GPS-IR;soil moisture;multi-satellites fusion;least squares support vector machine;estimated accuracy
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发布时间：2021-06-07 -
摘要：The semi-empirical kernel-driven linear bidirectional reflectance distribution function (BRDF) model is important and has been widely used in the remote sensing community. The hotspot signature is an important characteristic of the BRDF shapes and is commonly quantified by two degrees of freedom: the hotspot height and width near the hotspot direction. This research aimed to correct the hotspot effect of the Ross–Li BRDF model for potential users by correcting the Ross and Li kernels with an exponential function of two hotspot parameters ( C1/C2). This method has been developed in previous studies, but it was comprehensively applied to other kernel functions in the current study. Given the gap between leaves in the canopy, we corrected the overlap function of GO kernel with hotspot function. We analyzed the two hotspot parameters for the Ross–Li model by using the entire archive of POLDER BRDF database. First, we used six combinations of Ross and Li kernels to fit a typical single POLDER data for a specific analysis. We also analyzed the sensitivity of C1/C2 for these model combinations using the single POLDER pixel. Second, we used the entire POLDER dataset and acquired the optimum values of the hotspot parameters by using the root mean square error (RMSE) method. Finally, we analyzed the sensibility of the hotspot parameter in each model using 2D contour plots that distinctly show the variations in RMSEs as functions of C1 and C2. (1) The proposed hotspot parameterization method could be used to various combination models of Ross and Li kernels. The model with such a hotspot correction method improved the fitting ability of the hotspot signature better than the original model. (2) The optimum values of two hotspot parameters were significantly different between models, especially for the two geometric optical kernels, namely, LiSparseRChen (LSRC) and LiDenseRChen (LDRC). The value of C1 parameters in the LDRC models was generally smaller than that in the LSRC models. The possible reason could be that the LDRC kernel function modeled the hotspot effect on the canopy scale accurately, such that the role of the hotspot parameters (especially for C1) was secondary in this situation. (3) In general, the value of the C1 parameter in a single model was more sensitive to the variation in hotspot effect than the C2 parameter. This study comprehensively corrects the hotspot effect of the Ross–Li model for various applications for potential users who pay attention to the hotspot signatures of their applications. This study is also valuable for domestic multi-angle satellites in accurately reconstructing future hotspot signatures from multi-angle observations.关键词：remote sensing;BRDF;kernel-driven model;calibration of hotspot parameters;POLDER data;analysis of sensibility- 791
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发布时间：2021-06-07 - 摘要：Geometric positioning of satellite images, as one of the keys in producing geo-information products, plays an important role in development of national economic. In general, actual measured ground control data is necessary in geometric positioning of satellite images to correct systematic errors and then increase accuracy. However, the actual measured ground control data is high cost and difficult to obtained in some place. Considering lack of actual measured ground control data in some situations, a method of geometric positioning based on RFM with reference images to increase accuracy was proposed in this paper. The auxiliary control points extracted from high-precision images and digital elevation model (DEM) are used in geometric positioning based on RFM. Firstly, plenty of corresponding image points are matched from satellite images. The horizontal and elevation coordinates of the corresponding image points which named auxiliary control points are measured in high resolution reference images and elevation data and transformed into a unify coordinate system. Then, the auxiliary control points are used as ground control points with low accuracy in block adjustment without additional actual measured ground control data. Three kinds of satellite images including IKONOS images in Hobart, Australia, ZY-3 images in Sainte-Maxime, France, and TH-1 images in Dengfeng, China were used in the experiment in this paper, and the auxiliary control points named GE points were extracted from Google Earth data. The experiment divided into two parts, the first part was geometric positioning with the same amount of actual measured ground control points and GE points in the same position to analyze the accuracy of GE points, the result of which showing that there were systematic errors in both actual measured ground control points and GE points, the relation between two kinds of points depending on the quality of the satellite images. Another part of the experiment was block adjustment using GE points as ground control points and was designed in two cases, one was without actual measured ground control points and another was with few. It was found that the systematic errors were reduced and the accuracy were increased by proposed method in three experimental areas in both cases. Especially in the case without actual measured ground control points, the accuracy was increased obviously with plenty of GE points. Furthermore, the effect of the method was highly negatively correlated with the quality of the satellite images. The experimental result shows that the auxiliary control points extracted from reference images could be used as ground control points, proving the feasibility and availability of proposed method, which has good effect to increase accuracy of geometric positioning in the case without enough actual measured ground control data.关键词：remote sensing images;RFM;stereo geo-positioning;reference images;accuracy
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发布时间：2021-06-07 - 摘要：The classification technique plays an important role in the analysis of polarimetric synthetic aperture radar (PolSAR) images. PolSAR image classification is widely used in information extraction and scene interpretation or is performed as a preprocessing step for further applications. However, speckle noise appears in PolSAR images because of the coherent interference of waves reflected from elementary scatters. Such inherent speckle noise degrades the classification performance and brings difficulty for PolSAR image classification. Therefore, a novel supervised multi-feature-based classification method was proposed in this study. This method combined polarimetric signature information and spatial context information based on the random forest model. First, a modified simple linear iterative clustering algorithm was utilized to generate superpixels as classification elements by using the Pauli RGB image, which helped reduce speckle noise interference. Second, a high-dimensional polarimetric SAR feature image was constructed by collecting various polarimetric signatures generated by polarimetric decomposition and algebra operations. Then, the random forest model was trained on the basis of the PolSAR feature image by using training samples, and the number of classification votes of each decision tree in the random forest for each pixel was counted to compute the class probabilities of the superpixels. Finally, a neighborhood function was defined to express the spatial relationship among adjacent superpixels quantitatively, and the class probabilities of the superpixels were recalculated by the predefined neighborhood function in a Probabilistic Label Relaxation (PLR) procedure to reduce the interference of speckle noise. The final classification result was obtained by the maximum a posteriori decision rule when the iteration of PLR was terminated. Comparative experiments using different RADARSAT-2 images were conducted to evaluate the validity and applicability of the proposed method. The proposed approach achieved the highest accuracy (94.39% on the Flevoland RADARSAT-2 image and 85.09% on the Wuhan RADARSAT-2 image) and generated accurate and consistent classification results for the experimental images, which was considerably improved compared with those of other methods. Therefore, the proposed method can effectively suppress the interference of speckle noise by using superpixels and spatial context information and obtain accurate and consistent classification results for PolSAR images.关键词：remote sensing;polarimetric SAR;classification;multifeature;Random Forest;superpixels;probabilistic label relaxation
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发布时间：2021-06-07 -
摘要：Developments and customizations based on open source software have lower costs and higher flexibilities than the large-scale, high-cost, and high-complexity routine maintenance of commercial geographic information system (GIS) platforms that is limited by the function of the system. An increasing number of GIS applications are migrating from commercial platforms to open source GIS platform frameworks. The open source GIS platform facilitates the application of GIS and promotes the rapid development of spatial data management and processing technologies. However, developers may still face the challenge of scenarios of integration or mashup applications of tile datasets from multiple digital map sources in many GIS projects. Therefore, the data source of the base map based on the open source GIS platform must be increased to extend the space and time coverage of the base map in the study area. Cesium is an open source 3D map engine developed based on the Web Graphics Library with various characteristics, such as cross-platform, cross-browser, 2D/3D integration, and dynamic geospatial data visualization. This library offers a flexible and efficiency base map customization environment for WebGIS applications with low costs. Cesium provides access and loading support for two mainstream spatial references: WGS84 and Web Mercator coordinate systems. On the basis of these considerations, we comprehensively examined the tile data organization mechanisms and spatial reference differences of the Cesium platform and various tile data sources, such as Google maps, Tianditu, and Bing maps. Two types of tile data loading schemes, namely, static and dynamic loading, were designed for the mashup of multi-source tiles with different spatial references. (1) Experiment involving the single data source tile loading scheme illustrated that single data source tiles could be loaded successfully by the two image services, and the createTileMapServiceImageryProvider image service was suitable as a choice of single data source tile loading scheme. (2) The static and dynamic schemes showed favorable visualization effects when tiles from different data sources with the same spatial coordinate system were loaded. (3) The static tile mashup loading scheme could lead to tile image deformation when multi-source tile data with different spatial references were loaded. (4) The dynamic mashup scheme could overcome the deformation problem and shown better visualization effects than the static one when multi-source tile data with different spatial references were loaded. The proposed method that is based on Cesium and involves different spatial references and multi-source tile data in offline maps, data fusion, and integration applications can be used to extend the base map data source of GIS and remote sensing projects and has evident potential practical application values.关键词：remote sensing;Cesium;digital map;spatial data organization;tilling mashup;localized deployment- 745
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发布时间：2021-06-07 - 摘要：Mosaicked remote sensing images that cover large areas are important in image analysis and application. However, different degrees of color and contrast differences are observed between images due to the influence of sensor and external factors, such as light and fog, which complicate image mosaicking. Therefore, eliminating the differences between adjacent images and ensuring consistent colors in the large area (i.e., color balancing) are becoming increasingly significant. The acquisition cycle of remote sensing data is shortened and the amount of data is increased dramatically with the development of the sensor technology. The changes bring challenges to the efficiency of color balancing of remote sensing images. The traditional serial processing model based on CPU also cannot meet the requirements of fast processing mass data to handle emergency response. To solve the aforementioned problems, a parallel color balancing method based on adaptive block Wallis algorithm for image mosaicking was proposed. First, the images were adaptively divided into blocks depending on the coefficients of variation. Bilinear interpolation was used to determine the transformation parameters of each pixel, and the Wallis transform was adopted to eliminate the color differences between adjacent images. Second, Voronoi diagram was generated to determine the adjacent relation of images. Dijkstra algorithm was used to calculate the shortest path and determine the processing sequence for controlling the color consistency of the entire region. Finally, GPU technology was used to parallelize the proposed method for improving the efficiency. Bilinear interpolation and linear transformation are repetitive and dense computing tasks, which were directly assigned to each thread and executed simultaneously. The reduction method was adopted to parallelize the calculation of mean and standard deviation. Moreover, configuration, memory access, and instruction throughput were optimized to further improve the efficiency. Two groups of experiments were implemented on orthoimages to verify the effectiveness and efficiency of the proposed method. Experimental results showed that the proposed method was superior to the traditional Wallis method and Inpho in visual effect and quantitative evaluation. Moreover, the highest speed-up of the proposed parallel algorithm based on GPU could be more than 60 times that of the serial color balancing method based on CPU. The proposed method can effectively eliminate the color and contrast differences between adjacent images, thereby decreasing the difficulty in seamline detection. Meanwhile, the efficiency of the method is improved dramatically with the proposed parallel acceleration strategy. The performance of the proposed method is excellent in improving the quality and efficiency of color balancing and reducing the difficulty in image mosaicking. Moreover, the proposed method is sufficiently efficient to meet the requirements of fast color balancing of remote sensing images.关键词：remote sensing;color balancing;GPU parallel;adaptive block;Wallis transformation;reduction of sum
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发布时间：2021-06-07 -
摘要：Accurate extraction of temporal–spatial information of the enclosure culture area is important to the protection and sustainable development of a lake. In this study, we presented a new method to extract enclosure culture area with long-term Landsat time series. First, we collected all the available Landsat 5 and 8 scenes over Yangcheng Lake from 1984 to 2017 (a total number of scenes of 396). Then, a new algorithm using spectral and texture information was proposed to extract the enclosure culture area. Furthermore, a temporal filter was designed to smooth the time-series results for eliminating the deviation caused by data inconsistency between years. Finally, the manually determined enclosure culture area based on the Google Earth high-resolution image was used as the ground truth reference. Results showed that the producer’s accuracies of the final results were between 72.57% and 88.53%, whereas the user’s accuracies were between 79.79% and 98.10% for three selected years (i.e., 2002, 2010, and 2015). The enclosure culture area in Yangcheng Lake had experienced five stages: no-enclosure culture (1984–1994), rapidly growing (1994–1998), peak (1999–2002), rapidly declining (2003–2006), and stability (2007–2017), with the largest area up to 100 km2 and the current area maintained to 30 km2. By studying the vegetation index (normalized difference vegetation index in this study) of the enclosure culture area, we found that the amount of float vegetation grown significantly after 2002. Comparison with the water quality data showed that the water quality of Yangcheng Lake had not improved significantly since it reached IV level in 2003, although the enclosure culture area had been decreasing since 2002. Therefore, in the development of lake culture, the government should insist on sustainable development and develop the lake economy while maintaining the lake water quality.关键词：remote sensing, Yangcheng Lake;enclosure culture;Chinese mitten crab;water extraction;time-series;Landsat- 674
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发布时间：2021-06-07 -
摘要：The rapid development of high-spatial-resolution satellites has effectively alleviated the problem of mixed pixels in satellite images, thereby enabling extraction of the meticulous distribution of crops from them. The classification of remote sensing images is a quick way to obtain accurate agricultural information. However, the accuracy of supervised classification using remote sensing images is affected by several factors, such as classifier algorithm and input datasets. The imbalanced training samples, which indicates the number of training samples of some categories is considerably smaller or larger than the others, often results in poor classification accuracy for the minority classes. To improve this situation and generalization performance of classifier, this research focused on proper utilization of resampling techniques and classification methodologies for achieving perfect performance of remote sensing image classification. We investigated the aforementioned images by data mining approaches including spectrum and texture features and selection of optimized features based on recursive feature elimination. Then, five resample methods, namely, three over-resampling methods and two under-sampling methods, were separately used to balance the initial training datasets. Finally, we tested the resampled datasets by utilizing two classifiers (decision tree and AdaBoost) and evaluated the performance of each one in terms of kappa coefficient, overall accuracy, producer’s accuracy, and user’s accuracy. The overall classification accuracy and kappa coefficient improved considerably on decision tree (14.32%) and AdaBoost classifier (10.23%) after resampling. The AdaBoost obtained the highest value of kappa coefficient (0.9336) by using the training dataset resampled with ADASYN. The accuracy of classification on minority crops was also increased by resampling training datasets. Meanwhile, feature selection results showed that vegetation and texture indexes were more efficient than features of original reflection ratio to classification. Over-resampling methods had advantages in relieving the influence of imbalanced training samples to classifiers. Resampling process to training datasets has remarkable advantage in improving the classifier performance if the training datasets are critically imbalanced. The detailed accuracy assessment shows that over-resampling method is more excellent than under-resampling. The reason is that some significant samples are lost during under-resampling, but helpful and useful information is added after over-resampling. AdaBoost classifier performs better than decision tree in terms of solving imbalanced training datasets. Combination of proper resampling approaches and compatible classifier can significantly improve the accuracy of minority classes in the situation of imbalanced dataset classification.关键词：crops recognition;imbalanced datasets;resampling;remote sensing;minority crops;GF-2- 591
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- 5
发布时间：2021-06-07 -
摘要：Terrestrial Laser Scanning (TLS) can effectively describe complex forest scenes. This study aimed to classify ground point cloud within the height of 1.3 m into ground, vegetation, fallen wood, and standing trunk based on the TLS obtained from fallen wood plots in Daxing’anling. Fallen wood cloud point was segmented and merged. The optimal 3D neighborhood of each individual point was calculated through the Shannon entropy constructed by linearity, planarity, and scattering to avoid the difference in cloud density and the morphological characteristics introduced by occlusion. Shannon entropy could be maximized across the increasing kNN with an interval of 5 points. The optimal neighborhood size was used to compute the covariance eigenvalues for constructing 3D and 2D features. Key features were selected following the recursive feature elimination criteria, and a random forest classification algorithm was used to classify the points. Noise removal approach was applied to the fallen wood points classified by self-adjusting kNN features, and random sample consensus (RANSAC) segmentation was implemented to segment cylinders. Fallen wood cylinders were selected and merged depending on the axis direction less than 12° and the distance less than 0.1 m between each other. The overall classification accuracies of self-adjusting kNN method in plots A, B, and C were 93.17%, 94.52%, and 95.16%, respectively, and corresponding Kappa coefficients were 0.8771, 0.9145, and 0.9242, respectively. The overall accuracies of non-self-adjusting kNN were 92.65%, 89.09%, and 92.99%, and the Kappa coefficients were 0.8684, 0.8909, and 0.9299. Point cloud of plots B and C was classified using the model we trained using plot A. The classification accuracies of plots B and C were 62.38% and 59.80%, and the user precisions of fallen wood point cloud were 79.31% and 48.06%. All fallen woods had the same number as the ground measurement, and the parameters of fallen wood could be estimated roughly. Compared with the non-self-adjusting kNN method, the near-ground point cloud classification accuracy was improved by the self-adjusting kNN point cloud feature. Classification of plots B and C using the training result of plot A suggested that the selected key features in the complex forest could explain the dependent variable well. RANSAC could effectively segment the cylinder and estimate the parameters of the fallen wood. This research is significant for extracting parameters of the existing work. Further ecological research will be considered accordingly.关键词：Terrestrial Laser Scanning (TLS);point cloud;fallen wood;random forest classification;Random Sample Consensus (RANSAC)- 750
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- 7
发布时间：2021-06-07 -
摘要：Since 2003 when the Three-Gorge Dam (TGD) was in impoundment, the dam abundantly blocks suspended sediment and cause clear water flowing through the dam, which induces scouring effect on the beds and banks of the Yangtze river below the dam.Furthermore, the altered Suspended Sediment Concentration (SSC) has adversely affected the downstream coastal environment. In this study, the random forest model was applied for SSC estimation. The model is flexible and robust, and can be used for regression analysis of ecological environment variables. Yet, its ability in estimating SSC in aquatic environment has not been fully understood. On the basis of the monitoring data of SSC and satellite remote sensing reflectance data from 2002 to 2015, this study estimated the SSC in Yichang–Chenglingji downstream reach of the TGD by constructing a non-parametric regression model using random forest. The results showed that:(1) the random forest model could effectively monitor SSC, and the correlation coefficient and prediction accuracy were significantly improved from those of other models (linear regression, support vector machine, and artificial neural network model).(2) the red band is a suitable predictor for SSC in the random forest model, but cannot be independently used for forecasting. SSC remote sensing prediction requires multivariate co-participation. (3)By using the random forest model, the average root mean square error of the seasonal division was 0.46 mg/L, and the average relative root mean square error was 12.33%. These values met the needs of high-precision SSC estimation. In conclusion, this study reveals that the season shall be considered as temporal factors to estimate SSC and then prepare for the subsequent SSC spatiotemporal inversion. Which is of great help to reveal the TGD’s downstream river sediment evolution, and understand the regional distribution of sediment and sediment variation process in the future.关键词：TGD(Three Gorges Dam);downstream channel of dam;remote sensing of aquatic environment;Random Forest;remote sensing monitoring;MODIS;sediment inversion- 701
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发布时间：2021-06-07 - 摘要：Treatment of urban black and odorous water is important in the control of urban water environment. A remote sensing recognition model of black and odorous water bodies was constructed in this study to identify the affected water bodies for treatment, determine their location, and investigate their space distribution. On the basis of the analysis of formation mechanism of black and odorous water and test data, we constructed a spectrum index called water cleanliness index (WCI) from spectrum characteristics to reflect water cleanliness. We also established interpretation signs from image characteristics, such as water color, secondary environment, river silting, and riparian garbage stacking. Combination of the spectral index and interpretation signs could be used to identify black and odorous water bodies. The proposed method was used to identify the space distribution of black and odorous water bodies in the built-up area of Taiyuan. A total of 14 black and odorous river sections with a length of 52.530 km were obtained. The point accuracy of remote sensing identification of black and odorous water bodies was 92.86%, and the river accuracy of remote sensing identification of black and odorous water bodies was 78.19% without the effects of dried-up rivers. The weights of the spectral index and interpretation signs in the remote sensing identification of black and odorous water bodies were analyzed on the basis of the results of black and odorous river sections. The spectral index and water color had the largest weights among them with 29.60% and 27.10%, respectively. Both of them constituted the main characteristics of remote sensing identification of black and odorous water bodies. Comparison of the results of the two-phase remote sensing image recognition showed that WCI could significantly reflect the change characteristics of black and odorous water bodies. Therefore, this method has high precision in recognizing urban black and odorous water bodies by remote sensing and can be applied in the control of urban water environment.关键词：black and odorous water;remote sensing identification;water cleanliness index;interpretation symbol;Taiyuan city;WCI
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- 14
发布时间：2021-06-07 -
摘要：The Jiuzhaigou earthquake with the magnitude of 7.0 occurred in August 8, 2017 and resulted in a large number of landslides near the panda sea area in Jiuzhaigou. These landslides caused road congestion and seriously affected the progress of earthquake emergency rescue. The landslide caused by earthquake has wide distribution and large quantity. Given the urgency of the disaster and high resolution of unmanned aerial vehicle (UAV) images, the traditional artificial visual interpretation model cannot meet the needs of earthquake emergency response. Therefore, an automatic information identification method must be developed to identify the distribution range of landslide rapidly and accurately. On the basis of comprehensive analysis of the features of remote sensing images of landslide, an automatic information identification model for object-oriented analysis was constructed. First, the remote sensing images were segmented at different scales to obtain different levels of image objects depending on different types and scales of land objects. Then, SEath algorithm was used to construct feature rule set automatically by the comprehensive utilization of the information of spectrum, texture, and shape of object at every level, and the distribution of earthquake-induced landslides was identified. Thereafter, the accuracy and efficiency of recognition were evaluated on the basis of artificial visual interpretation. Finally, the spatial distribution features of landslide body in topographic factor and fracture distribution layer were analyzed by statistical analysis. Using the acquired aerial image data of UAV, the earthquake landslide near the panda sea area of Jiuzhaigou earthquake was identified. The overall accuracy was 94.8%, and the Kappa coefficient was 0.827. The present method was twice as efficient as the artificial visual interpretation method under the same configuration of computer. The spatial distribution of landslide was positively related to slope and topographic relief but was negatively correlated with roughness. No evident relationship was found between the spatial distribution of landslide mass and the topographic factors such as slope and gradient of slope. Evident fault effects were observed in the distribution of landslide. In this study, the object-oriented analysis method was developed to realize automatic identification of earthquake-induced landslide using UAV images. On the basis of the comprehensive utilization of spectrum, texture, and shape of image objects at each segmentation level, an automatic construction method of feature rule set based on SEaTH algorithm was established, Finally, an automatic, efficient extraction of earthquake-induced landslides was realized. Compared with the artificial visual interpretation method, the automatic method of object-oriented analysis could effectively improve the efficiency and timeliness of disaster information identification after earthquake, which could break the pattern of multiple interpretations and save time for earthquake emergency response. The earthquake-induced landslide distribution features in elevation, slope, aspect, fault distance, and other factors were also analyzed. The correlation between landslide and topographic factors was found. Overall, the earthquake-induced landslide in the study area is mainly controlled by the Tazang fault. The spatial distribution rule can provide information support for landslide risk assessment, disaster investigation, prediction, and prevention.关键词：Jiuzhaigou earthquake;unmanned aerial vehicle image;object-oriented;landslide;earthquake emergency- 647
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- 17
发布时间：2021-06-07 -
摘要：Land surface thermal environment is an important factor in the surface ecological environment and is related to human survival and society development. Similar to the heat island effect in urban areas, some high temperatures are detected beside active faults in the region of natural surface and are affected by the lithology, soil, and vegetation. Therefore, these geological and geographical factors impact the land surface thermal environment. In our research, we investigated the distribution characteristics of surface thermal environment in Zhejiang Province on the basis of land surface temperature through thermal infrared remote sensing of Landsat 8 OLI/TIRS images. We analyzed the effects of fault activity, lithology, soil, and vegetation on the land surface thermal environment by using multiple linear regression analysis. Results showed that the active faults could cause thermal anomalies within a certain distance, the rocks and soils could affect land surface temperature depending on their cover types, and the vegetation coverage could reduce the effect of high temperature in the surface thermal environment. In conclusion, the proposed method is effective for tectonic activity monitoring and can serve as a scientific basis of research on ecological environment.关键词：inferred remote sensing;surface thermal environment;fault zones- 681
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发布时间：2021-06-07
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