IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing最新文献

{"title":"A Multiview Interclass Dissimilarity Feature Fusion SAR Images Recognition Network Within Limited Sample Condition","authors":"Benyuan Lv;Jiacheng Ni;Ying Luo;S. Y. Zhao;Jia Liang;Hang Yuan;Qun Zhang","doi":"10.1109/JSTARS.2024.3457022","DOIUrl":"10.1109/JSTARS.2024.3457022","url":null,"abstract":"In Synthetic aperture radar (SAR) recognition tasks, due to its special imaging principle, SAR images acquired from different viewpoints contain target features that may carry a large amount of information. However, if recognition is forced by fusion of multiview features when raw data is scarce, feature redundancy will be formed, which in turn will lead to a decrease in recognition accuracy. To remedy the above problem, a multiview inter-class dissimilarity feature fusion (MIDFF) network is proposed in this study. The proposed network has multiple parallel inputs and can extract multiview features and heterogeneous features. Firstly, a method for rapidly generating sufficient training data for MIDFF is proposed, which generates training data by repeatedly combining images from different views and classes to ensure that a large number of training inputs are available even when raw SAR images are scarce. Secondly, a method of calculating and enhancing of inter-class dissimilarity (ICD) features is proposed to increase the inter-class distance and improve the inter-class separability. Then, the ICD and multiview features are fused to increase the features learned by the network and reduce feature redundancy. Finally, a multiview heterogeneous weighted loss function is proposed, which combines the calculation of inter-class similarity and heterogeneous loss. Through the gradual convergence of the loss function, the inter-class similarity decreases as the loss decreases, which further improves the target recognition performance. Experimental results on MSTAR and Civilian Vehicle SAR datasets show that our proposed method performs better than the state-of-the-art methods within sample scarcity conditions.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10670287","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193211","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DESIS Hyperspectral Satellite Data for Cropping Pattern Classification","authors":"Mbali Mahlayeye, Roshanak Darvishzadeh, Charlynne Jepkosgei, Kelvin Mlawa, Andrew Nelson","doi":"10.1109/jstars.2024.3457791","DOIUrl":"https://doi.org/10.1109/jstars.2024.3457791","url":null,"abstract":"","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":5.5,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193214","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Surface Reflectance From Commercial Very High Resolution Multispectral Imagery Estimated Empirically With Synthetic Landsat (2023)","authors":"Paul M. Montesano;Matthew J. Macander;Jordan Alexis Caraballo-Vega;Melanie J. Frost;Christopher S. R. Neigh;Gerald V. Frost;Glenn S. Tamkin;Mark L. Carroll","doi":"10.1109/JSTARS.2024.3456587","DOIUrl":"10.1109/JSTARS.2024.3456587","url":null,"abstract":"Scientific analysis of Earth's land surface change benefits from well-characterized multispectral remotely sensed data for which models estimate and remove the effects of the atmosphere and sun-sensor geometry. Top-of-atmosphere (TOA) reflectance in commercial very high resolution (<5 m; VHR) spaceborne imagery routinely varies for unchanged surfaces because of signal variation from these effects. To reliably identify critical broad-scale environmental change, consistency from surface reflectance (SR) versions of this imagery must be sufficient to identify and track the change or stability of fine-scale features that, though small, may be widely distributed across remote and heterogeneous domains. Commercial SR products are available, but typically the model employed is proprietary and their use is prohibitively costly for large spatial extents. Here, we 1) describe and apply an open-source workflow for the scientific community for fine-scaled empirical estimation of SR from multispectral VHR imagery using reference from synthetic Landsat SR, 2) examine SR model results and compare with corresponding TOA estimates for a large batch with varying acquisitions in Arctic and Sub-Arctic regions, 3) assess its consistency at pseudoinvariant calibration sites, and 4) quantify improvements in classification of land cover in a Sahelian region. Results show this workflow is best for longer wavelength optical bands, identifies poor estimates associated with image acquisition variation using context provided from large batches of VHR, improves estimates with robust regression models, produces consistent estimates for non-varying sites through time, and can increase the accuracy of land cover assessments.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10670299","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193202","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Nonlocal Filter for SAR Interferometric Phase Based on Partial Siamese Network","authors":"Yanming Chen;Fan Zhang;Lixiang Ma;Yongsheng Zhou;Qiang Yin","doi":"10.1109/JSTARS.2024.3458075","DOIUrl":"10.1109/JSTARS.2024.3458075","url":null,"abstract":"Filtering is a crucial step in synthetic aperture radar (SAR) interferometric phase processing. In the context of rapid development in deep learning, the interferometric phase filtering networks have demonstrated more powerful filtering capabilities than spatial and transform domain filtering models. Inspired by the deep learning-based and nonlocal filtering models, this article proposes a nonlocal SAR interferometric phase filtering model based on a partial siamese network. Within this model, the interferogram is decomposed into multiple patches, and each patch undergoes filtering using a set of structurally identical encoder–decoder networks. In order to make better use of the nonlocal characteristics of the interferogram to improve the filtering effect, the encoder–decoder networks corresponding to different patches share a portion of their weights. Finally, the filtered patches outputted from each decoder are concatenated through an aggregation block to obtain the complete filtered interferogram. The advantage of this framework lies in its ability to fully leverage the powerful feature learning capabilities of neural networks while maximizing the utilization of nonlocal characteristics in the interferogram. The filtering performance of the proposed method is tested through simulated interferograms and real-world interferograms obtained from the Sentinel-1 and Gaofen-3 mission. The experimental results demonstrate that compared to traditional spatial and transform domain filtering methods, as well as state-of-the-art deep learning-based and nonlocal filtering methods, the filtering approach proposed in this article achieves superior filtering performance.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10675328","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Performance Analysis and System Design in GEO-LEO Bistatic SAR","authors":"Faguang Chang;Dexin Li;Zhen Dong","doi":"10.1109/JSTARS.2024.3457821","DOIUrl":"10.1109/JSTARS.2024.3457821","url":null,"abstract":"The geosynchronous-low-Earth-orbit bistatic synthetic aperture radar (GEO-LEO BiSAR) adopts the transceiver split system form, in which GEO SAR serves as the illuminator and LEO SAR serves as the receiver. GEO-LEO BiSAR can obtain abundant target scattering information, can realize large width imaging and reduce the receiver cost. Three parameters are selected as measurement indicators to comprehensively evaluate system performance and provide system design guidance. The spatial resolution can measure the imaging performance, the radiation resolution is the ability to distinguish the objects scattering characteristics, and the beam coverage area represents the system observation ability. They are all key parameters and are closely related to the BiSAR configuration. In this article, we use the generalized ambiguity function and integral equation model to derive the BiSAR spatial and radiation resolution expressions, and the beam coverage area calculation method is derived through geometric knowledge. These three parameters are modeled as objective functions for the system design of the multiobjective optimization problem, in which multiobjective evolutionary algorithm based on decomposition and differential evolution is used to solve the receiver orbital element. We can optimize these three parameters jointly considering their values or optimize the resolution (spatial and radiation resolution) and coverage area in sequence. Finally, the analysis correctness is verified by simulation experiments. The proposed system design method comprehensively considers the ability of observation, resolution and recognition, and can reasonably select joint or sequential optimization schemes according to the system capability requirements to guide the receiver orbital element selection.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10675347","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LT-1A/B Satellite SAR Geometric Calibration and Absolute Location Error Analysis","authors":"Minzheng Mu;Zhiwei Li;Bing Xu;Xin He;Kun Han;Xun Du;Qijin Han;Aichun Wang","doi":"10.1109/JSTARS.2024.3456813","DOIUrl":"10.1109/JSTARS.2024.3456813","url":null,"abstract":"LuTan-1 (LT-1) is China's first L-band differential interferometric synthetic aperture radar (SAR) satellite system, and also the first twin L-band SAR satellite system in the world. Geometric accuracy is one of the most crucial indicators for remote sensing satellites. SAR images with high geometric accuracy not only establish a more accurate geometric correspondence between image pixels and actual ground objects but also greatly simplify the later application. This article primarily explores the geometric error issues of the LT-1 satellite system, encompassing analysis of error sources, error modeling, and correction. Through research conducted on 232 acquisitions of LT-1 covering three calibration arrays across the globe during the in-orbit commissioning phase, it is shown that the direct out-of-box LT-1 SAR imagery exhibits a slant range deviation of approximately 38 m and an azimuth deviation of about 15 m. After applying the correction methods described in this article, the range accuracy is improved to 0.7 m, and the azimuth accuracy is enhanced to 2.1 m. In addition, we found that after correcting for atmospheric delay, there still exists a correlation between the slant range errors and beams. Optimal correction results can only be achieved through beam-wise calibration. Since the LT-1 operates in the L-band SAR frequency range, where ionospheric propagation delay is significant. We analyzed the relationship between ionospheric delay estimation error and slant range error. Our analysis revealed that the accuracy of ionospheric delay estimation is the primary factor limiting the precision of the slant range.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10670297","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamic Convolution Covariance Network Using Multiscale Feature Fusion for Remote Sensing Scene Image Classification","authors":"Xinyu Wang;Furong Shi;Haixia Xu;Liming Yuan;Xianbin Wen","doi":"10.1109/JSTARS.2024.3456854","DOIUrl":"10.1109/JSTARS.2024.3456854","url":null,"abstract":"The rapid increase in spatial resolution of remote sensing scene images (RSIs) has led to a concomitant increase in the complexity of the spatial contextual information contained therein. The coexistence of numerous smaller features makes it challenging to accurately locate and mine these features, which in turn makes accurate interpretation difficult. In order to address the aforementioned issues, this article proposes a dynamic convolution covariance network (ODFMN) based on omni-dimensional dynamic convolution, which can extract multidimensional and multiscale features from RSIs and perform statistical higher-order representation of feature information. First, in order to fully exploit the complex spatial context information of RSIs and at the same time improve the limitation of a single static convolution kernel for feature extraction, we constructed a omni-dimensional feature extraction module based on dynamic convolution, which fully extracts the 4-D information within the convolution kernel. Then, to make full use of the full-dimensional feature information extracted from each level in the network, the feature representation is enriched by constructing multiscale feature fusion module to establish relationships from local to global. Finally, higher order statistical information is employed to address the challenge of representing first-order information for smaller object features, which is inherently difficult to do. Experiments conducted on publicly available datasets have demonstrated that the method achieves high classification accuracies of 99.04%, 95.34%, and 92.50%, respectively. Furthermore, the method has been verified to have high capture accuracy for feature target contours, shapes, and spatial context information through feature visualization.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10670278","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193207","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Inverse Dynamic Parameter Identification for Remote Sensing of Soil Moisture From SMAP Satellite Observations","authors":"Runze Zhang;Adam C. Watts;Mohamad Alipour","doi":"10.1109/JSTARS.2024.3457941","DOIUrl":"10.1109/JSTARS.2024.3457941","url":null,"abstract":"In the soil moisture active passive (SMAP) mission's soil moisture retrieval algorithms, the effects of surface roughness and vegetation scattering on the brightness temperature are conventionally modeled using time-invariant parameters: roughness intensity (\u0000&lt;italic&gt;h&lt;/i&gt;\u0000) and effective scattering albedo (ω). Such simplification neglects the variability of \u0000&lt;italic&gt;h&lt;/i&gt;\u0000 and ω over time, potentially compromising the accuracy of soil moisture estimates at the satellite footprint scale. This study aims to derive dynamic, pixel-scale \u0000&lt;italic&gt;h&lt;/i&gt;\u0000 and ω parameters specifically for the SMAP single-channel algorithm (SCA) and the regularized dual-channel algorithm (RDCA). This is achieved through an iterative inverse procedure that minimizes the differences between the simulated brightness temperatures from spatially representative 9 km soil moisture and SMAP observations across the SMAP core validation sites. The results demonstrated that the incorporation of dynamic \u0000&lt;italic&gt;h&lt;/i&gt;\u0000 and ω parameters, derived on a daily scale, markedly enhanced the soil moisture retrieval performance with an average unbiased root-mean-square error (ubRMSE) of 0.01 (0.02) m\u0000&lt;sup&gt;3&lt;/sup&gt;\u0000/m\u0000&lt;sup&gt;3&lt;/sup&gt;\u0000 and Pearson correlation (\u0000&lt;italic&gt;R&lt;/i&gt;\u0000) of 0.95 (0.90) for the SCA (RDCA) algorithms, indicating that dynamic parameterization holds significant promise for improving retrieval accuracy. The daily scale \u0000&lt;italic&gt;h&lt;/i&gt;\u0000 parameters are generally above the static values utilized in the SMAP SCA. Within the SMAP SCA framework, the accuracy of soil moisture estimates employing daily scale \u0000&lt;italic&gt;h&lt;/i&gt;\u0000 and ω parameters—randomly selected from the SCA range (\u0000&lt;italic&gt;h&lt;/i&gt;\u0000∊ [0.03, 0.16] and ω∊ [0, 0.08])—demonstrates notable stability and is comparable with the SMAP level 3 product. Furthermore, the daily scale parameters were temporally contracted to generate a monthly climatology for \u0000&lt;italic&gt;h&lt;/i&gt;\u0000 and ω. While soil moisture values derived from these climatological \u0000&lt;italic&gt;h&lt;/i&gt;\u0000 and ω parameters exhibit reduced absolute bias, their ubRMSE and \u0000&lt;italic&gt;R&lt;/i&gt;\u0000 slightly degrade relative to SMAP level 3 product. This degradation likely suggests that the climatological parameters’ gradual variations are insufficient to capture the fluctuations of those daily parameters. Moreover, the static \u0000&lt;italic&gt;h&lt;/i&gt;\u0000 and ω values for the RDCA are systematically higher than those for the SCA. However, there is no consistent trend in the magnitudes of dynamic \u0000&lt;italic&gt;h&lt;/i&gt;\u0000 and ω between different algorithms. Identifying the most effective dynamic \u0000&lt;italic&gt;h&lt;/i&gt;\u0000 and ω parameters within the SMAP algorithmic framework necessitates not only selecting an appropriate parameter range but also accurately tracking the temporal evolutions of surface roughness and vegetation scattering. Potential applications arising from improvements in retrieved soil moisture include the management of agricultural lands and forecasts of their productivity, quantification of global water and energy flux","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10675324","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SFMRNet: Specific Feature Fusion and Multibranch Feature Refinement Network for Land Use Classification","authors":"Guojun Chen;Haozhen Chen;Tao Cui;Huihui Li","doi":"10.1109/JSTARS.2024.3456842","DOIUrl":"10.1109/JSTARS.2024.3456842","url":null,"abstract":"Land use classification of high-precision satellite images using semantic segmentation methods has become mainstream. In this field, global context information plays an irreplaceable role. However, most current methods struggle to effectively utilize this global context, which results in low segmentation accuracy, especially in scenes with similar objects, small targets, or obscured by shadows. To address the above issues, this article introduces SFMRNet—the network that integrates the advantages of Transformer and convolutional neural network (CNN)—and designs various modules to utilize the power of contextual information as much as possible. First, we design a specific enhanced feature fusion module (SEFFM) that selectively enhances spatial or channel information of feature maps before fusion, effectively mitigating small interclass differences. Second, our proposed multibranch feature refinement module (MFRM) facilitates the interaction between different feature layers and refines these features to enhance multiscale characterization. This improves the segmentation of small-sized targets and addresses the occlusion issues. Finally, comprehensive testing and detailed ablation analysis are conducted on three datasets: the ISPRS Vaihingen, ISPRS Potsdam, and LoveDA land use classification datasets. The results demonstrate that SFMRNet exhibits superior segmentation capabilities compared to existing advanced methods.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10670291","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spectral-Enhanced Sparse Transformer Network for Hyperspectral Super-Resolution Reconstruction","authors":"Yuchao Yang;Yulei Wang;Hongzhou Wang;Lifu Zhang;Enyu Zhao;Meiping Song;Chunyan Yu","doi":"10.1109/JSTARS.2024.3457814","DOIUrl":"10.1109/JSTARS.2024.3457814","url":null,"abstract":"Hyperspectral image (HSI) has garnered increasing attention due to its capacity for capturing extensive spectral information. However, the acquisition of high spatial resolution HSIs is often restricted by current imaging hardware limitations. A cost-effective approach to enhance spatial resolution involves fusing HSIs with high spatial resolution multispectral images collected from the same scene. Traditional convolutional neural network-based models, although gained prominence in HSI super-resolution reconstruction, are typically limited by their small receptive field of the convolutional kernel, primarily emphasizing local information while neglecting nonlocal characteristics of the image. In light of these limitations, this article proposes a novel spectral-enhanced sparse transformer (SEST) network for HSI super-resolution reconstruction. Specifically, the proposed SEST employs a sparse transformer to capture nonlocal spatial similarities efficiently, along with a spectral enhancement module to learn and exploit spectral low-rank characteristics. Integrated within a multiwindow residual block, the abovementioned two components collaboratively extract and combine distinct fine-grained features through a weighted linear fusion process, facilitating the integration of spatial and spectral information to optimize the reconstruction result. Experimental results validate the superior performance of the proposed SEST model against current state-of-the-art methods in both visual and quantitative metrics, thus confirming the effectiveness of the proposed approach.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10675340","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142193204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}