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

{"title":"Hierarchical Fusion Transformer for Multimodal Ground-Based Cloud Type Classification","authors":"Shuang Liu;Zeyu Yu;Zhong Zhang;Chaojun Shi;Baihua Xiao","doi":"10.1109/JSTARS.2025.3614756","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3614756","url":null,"abstract":"Existing methods for multimodal ground-based cloud type classification are dominated by convolutional neural networks, and it fails to capture long-range dependencies. In this article, we propose a novel Transformer-based architecture named hierarchical fusion transformer (HFT) for multimodal ground-based cloud type classification, which leverages the advantages of self-attention and cross-attention to learn long-range dependencies and effectively fuse cloud images and meteorological element information. Specifically, we propose visual and meteorological joint-transformer (VM Joint-Trans) to capture global context across modalities and present visual and meteorological cross-transformer (VM Cross-Trans) to align different modalities and reduce their inconsistencies. We design a hierarchical architecture to perform comprehensive fusion using VM Joint-Trans and VM Cross-Trans. Meanwhile, we propose the novel multimodal contrastive learning, which not only constrains the tokens of cloud images and meteorological element information in the same layer, but also the tokens from the same modality in different layers, thereby improving the discriminative ability of model and reducing the modality gap. Furthermore, we release the large-scale multimodal ground-based cloud database, containing 10 000 multimodal samples with seven categories. To the best of the authors’ knowledge, it is the largest database for multimodal ground-based cloud type classification. Experimental results validate the effectiveness of the proposed HFT for multimodal ground-based cloud type classification.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"25192-25203"},"PeriodicalIF":5.3,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11181175","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145255893","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":"Assessment of Urban and Peri-Urban Green Infrastructure Patterns Using Morphological Spatial Pattern Analysis and Satellite Imagery: Case Studies of Braşov and Oradea, Romania","authors":"Mostafa Alahmad;Ruxandra-Georgiana Postolache;Ioan Adrian Timofte;Iosif Vorovencii","doi":"10.1109/JSTARS.2025.3614809","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3614809","url":null,"abstract":"Sustainable urban development requires a detailed understanding of green infrastructure (GI) and its spatial patterns. Urban growth can occur within existing urban areas or through expansion into peri-urban zones. Maintaining a high percentage of GI contributes to well-being by regulating microclimatic parameters, reducing air pollution, decreasing urban noise, and supporting public health by providing access to functional green spaces. This study aimed to assess the GI patterns in the urban and peri-urban areas of two Romanian cities, Braşov and Oradea, using morphological spatial pattern analysis (MSPA). Data for evaluating GI patterns were derived from multitemporal Sentinel-1 (S-1) and Sentinel-2 (S-2) satellite imagery, alongside vegetation indices (NDVI, EVI, NDBI, SAVI, and NDWI). Classification was performed into seven land use/land cover (LULC) classes using the gradient tree boosting machine learning algorithm, achieving overall accuracies of 96.02% for Braşov and 95.67% for Oradea. The LULC categories were reclassified into foreground (forest, grassland, cropland, and water) and background (built-up, uncultivated, and bare land). Seven MSPA classes (core, edge, bridge, branch, islet, perforation, and loop) were evaluated to measure GI’s morphological patterns, and Gini coefficients were calculated to assess GI equity. Results showed that Braşov had a moderately equitable GI distribution (0.325) in urban and peri-urban areas, while Oradea displayed a more unequal distribution (0.519). Core areas represented the largest spatial extent in both cities, with Braşov covering 73.1% (urban) and 84.1% (urban and peri-urban), while Oradea covered 59.0% (urban) and 66.5% (urban and peri-urban). The GI edge pattern in Oradea was more complex, indicating higher fragmentation.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"25087-25109"},"PeriodicalIF":5.3,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11181181","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145255816","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":"View Intervention and Feature Alignment Aggregation Framework for Multiview SAR Target Recognition","authors":"Qijun Dai;Gong Zhang;Biao Xue;Lifeng Liu;Lipo Wang","doi":"10.1109/JSTARS.2025.3614695","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3614695","url":null,"abstract":"Multiview synthetic aperture radar (SAR) automatic target recognition (ATR) has attracted increasing attention for its ability to integrate effective information from multiple images. However, the existing algorithms have ignored the interplay between the multiview combination and the multiview network, failing to explore the inherent coupling relationship within multiview images. To tackle these issues, a multiview SAR ATR framework called view intervention and feature alignment aggregation is proposed. First, a deep clustering-based multiview combination is designed. Images with sufficient complementary information are selected from the raw SAR data under each category to form multiview images according to image features, which are the latent features obtained by the autoencoder (AE). Next, an efficient multiview feature alignment aggregation (Mv-FAA) network is proposed, in which the encoder of the AE serves as the feature extraction module. By designing a hybrid loss function to guide the training of the Mv-FAA network, it can extract complementary features from multiview images while retaining certain consistent features so that the final holistic features of the target are obtained for discrimination. The proposed framework strengthens the link between the multiview combination and the multiview network to reconcile the complementary and consistent information within multiview images, providing valuable insights for advancing multiview SAR ATR research. The experimental results on the Moving and Stationary Target Recognition and the Full Aspect Stationary Targets-Vehicle datasets have achieved state-of-the-art performance.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"25177-25191"},"PeriodicalIF":5.3,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11181158","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145255838","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 Benchmark Dataset and Novel Methods for Parallax-Based Flying Aircraft Detection in Sentinel-2 Imagery","authors":"Beibei Song;Nan Mao;Jingyuan Li;Wenwang Du;Zhe Wang;Yingzhao Shao;Xiaobo Li;Qiudie Bao;Xiaohan Wang;Wenfang Sun","doi":"10.1109/JSTARS.2025.3615068","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3615068","url":null,"abstract":"Satellite-based aircraft monitoring is an important complement to ground surveillance systems, providing strong support for the safe, efficient, and reliable operation of global aviation. Most existing aircraft detection datasets are derived from still satellite imagery, making it difficult to detect flying aircraft. Although video satellite imagery can provide motion cues, its spatial coverage is limited, making it challenging to capture flying aircraft targets that are sparsely distributed over wide areas. Each Sentinel-2 satellite image covers a width of hundreds of kilometers, providing favorable conditions for monitoring flying aircraft. Beyond this, the physical design of its multispectral instruments induces parallax effects for moving objects in multispectral imagery, enabling a novel approach for the detection of flying aircraft. We construct a flying aircraft detection dataset (S2Aircraft) based on Sentinel-2 satellite multispectral imagery with a spatial resolution of 10 m. The dataset is annotated with oriented bounding boxes and includes both RGB and NIR spectral bands. In addition, we design an efficient flying aircraft detection network (FADet), which maps input images to a high-dimensional nonlinear feature space while maintaining low computational complexity. Moreover, for single-class object detection tasks, the model employs a semidecoupled head to achieve efficient detection. Finally, a loss function is specifically designed according to the geometric characteristics of targets in the S2Aircraft dataset, significantly improving the accuracy and stability of oriented object detection. Extensive experiments demonstrate the effectiveness and advancement of our FADet. Specifically, on our S2Aircraft dataset, FADet achieves competitive performance reaching 2.6 giga floating-point operations per second and 96.3% mean average precision (mAP) at 50% intersection over union. On two public datasets, HRSC2016 and CORS-ADD, FADet achieves mAP<inline-formula><tex-math>$_{50}$</tex-math></inline-formula> of 90.90% and 94.16%, respectively.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"25221-25234"},"PeriodicalIF":5.3,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11180886","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145255837","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":"IEEE Geoscience and Remote Sensing Society Information for Authors","authors":"","doi":"10.1109/JSTARS.2025.3614276","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3614276","url":null,"abstract":"","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"C3-C3"},"PeriodicalIF":5.3,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11182280","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145141612","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":"Mapping the Spatiotemporal Patterns of Surface Mining in Resource-Based Cities and Assessing Their Impact on Land Surface Temperature Across the Urban–Rural Gradient","authors":"Xiaoquan Pan;Zilong Xia;Hong Fang;Haowei Mu;Bo Yuan;Shanchuan Guo;Peijun Du","doi":"10.1109/JSTARS.2025.3614249","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3614249","url":null,"abstract":"Intensive mining activities in resource-based cities can significantly alter the local thermal environment. However, due to the dynamic characteristics and heterogeneous backgrounds of mining areas, it remains challenging to delineate their spatiotemporal extent and assess their impacts. To address this issue, this study integrated multisource remote sensing data with NDVI and land surface temperature (LST) retrieval methods to map the dynamics of different types of surface mines from 1990 to 2020 in the Hohhot–Baotou–Ordos–Yulin (HBOY) urban agglomeration and to systematically assess their impacts on LST along the urban–rural gradient. A zonal threshold segmentation approach was developed to delineate mining areas under complex backgrounds, while a hierarchical overlay analysis framework within an urban–interface–rural structure was proposed to systematically evaluate the thermal impacts of these different mining types. The results show that surface mining in HBOY expanded exponentially over the past three decades, with surface coal mines mainly distributed in Ordos (72% ) and Yulin (16.87% ), whereas 96% of noncoal surface mines were concentrated in Baotou. Between 1990 and 2020, surface mining cumulatively disturbed 1,175.1 km² of land, of which 66.43% was grassland and 25.14% was barren land. Mining activities induced distinct thermal responses along the urban–rural gradient. Surface coal mines primarily occupying grassland tended to cause warming, while noncoal surface mines primarily occupying barren land tended to reduce LST. These findings reveal the thermal propagation patterns of mining activities along the urban–rural gradient and offer important insights for balancing resource development and environmental protection in resource-based cities.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"25248-25263"},"PeriodicalIF":5.3,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11178150","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145255871","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":"FlexShip: A More Flexible Network for Small Target Detection on Marine Ships","authors":"Haorui Gu;Ailian Bian","doi":"10.1109/JSTARS.2025.3614583","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3614583","url":null,"abstract":"Remote sensing ship detection is of great significance to the fields of maritime traffic management and marine resource monitoring. Although the latest ship target detection models have shown excellent results, they still face many challenges. We summarize the three key challenges that ship target detection still faces: 1) Due to the resolution limitation of remote sensing images and the small size of ships, the targets show weak edges in the images and are difficult to accurately identify. 2) The sea surface environment is changeable, often accompanied by interference information such as ripples, clouds, and wake waves, which can easily cause false detection and missed detection. 3) The length-width ratio of ships is very different, and the directions are different. Conventional rectangular frames are difficult to accurately envelop targets, resulting in inaccurate positioning and background redundancy. To address the abovementioned problems, this article proposes a new method called FlexShip. Specifically, first, the self-correcting convolution mechanism is applied to remote sensing image target detection. The network adaptively modifies the feature response strength of different scales and contrast regions through dynamic convolution kernel parameter calibration, enhancing the expression ability of weak ship edge structures. Second, as a basic element of the feature enhancement method, we also create a feature-guided attention module to guide the network to focus on the key texture and contour data of the ship area. Finally, polar coordinates are used to move the prediction box. Compared with the rectangular coordinate system, the polar coordinate system has more adjustable rotation angles and fewer parameters. Extensive experiments on multiple public datasets show that the proposed FlexShip achieves state-of-the-art performance in ship target detection.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"25291-25304"},"PeriodicalIF":5.3,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11180820","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145255849","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":"Aerosol Optical Depth Retrieval Over Ocean and Land From Fengyun-3F MERSI-III: First Results and Validation","authors":"Leiku Yang;Xin Pei;Yidan Si;Xiuqing Hu;Yizhe Fan;Weiqian Ji;Ping Zhang;Ling Wang;Xiaofeng Lu;Xiaoqian Cheng","doi":"10.1109/JSTARS.2025.3613696","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3613696","url":null,"abstract":"The Fengyun-3F (FY-3F) satellite, China’s latest launched polar-orbiting meteorological satellite with morning overpass, carries the advanced Medium Resolution Spectral Imager-III (MERSI-III). Sharing similar characteristics with the Moderate Resolution Imaging Spectroradiometer (MODIS), MERSI-III/FY-3F has the potential to fill the observational gap in morning-orbit aerosol monitoring once Terra’s mission concludes. This study adapts the aerosol retrieval algorithm originally developed for the MERSI-II sensor aboard the FY-3D satellite to the MERSI-III sensor. Algorithm improvements have been made to accommodate MERSI-III’s characteristics, primarily focusing on optimizing the surface reflectance estimation model and updating the lookup tables. The improved algorithm has been applied to global MERSI-III observations for the entire year of 2024, and the first aerosol optical depth (AOD) retrieval results over ocean and land for this sensor have been produced and validated. The results show that MERSI-III AOD retrievals agree well with Aerosol Robotic Network data, with 69.8% and 71.0% of retrievals falling within expected error (EE) envelopes over ocean [± (0.03 + 10% )] and land [± (0.05 + 20% )], respectively. The accuracy of MERSI-III retrievals is comparable to that of Terra MODIS dark target products (EE: 65.3% over ocean, 73.4% over land). Comparative analysis of collocated MERSI-III and MODIS AOD datasets reveals consistent spatial patterns, although regional biases are observed in areas with high aerosol loading and near desert fringes. These results indicate that MERSI-III/FY-3F can provide reliable global aerosol observations and is expected to serve as an important candidate for the AOD data record from the morning-orbiting Terra/MODIS satellite.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"25235-25247"},"PeriodicalIF":5.3,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11176967","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145255894","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":"An Adaptive Physical-World Adversarial Patch for Remote Sensing Image Object Detection Models Considering the Structural Characteristics","authors":"Xichen Xing;Xiong Xu;Qian Shi;Yanmin Jin;Chao Wang","doi":"10.1109/JSTARS.2025.3613373","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3613373","url":null,"abstract":"Remote sensing image object detection represents a typical application in the field of remote sensing image processing. Rapid advancements in artificial intelligence have established deep learning as a prevalent method for detecting critical targets, such as aircraft, vehicles, and vessels, in remote sensing imagery. However, empirical evidence indicates that deep learning networks frequently exhibit vulnerabilities that minimal pixel perturbations in digital images can compromise their output. Adversarial attack techniques exploit this vulnerability by adding imperceptible perturbations to input data, causing erroneous outputs in deep learning models, which compromises object detection accuracy. Such attacks could be applied in military or civilian domains, such as enhancing airport security systems to prevent confidential information leaks. However, existing physical attack methods in remote sensing are limited by their lack of consideration for dynamic factors such as target characteristics and distortions. This article introduces an adaptive adversarial attack framework with emphasis on representative targets such as aircraft, which generates aircraft-attachable patches by incorporating target-specific structural characteristics. The research especially designed cross-shaped patches and improved the loss function by incorporating gradient variance loss and image quality assessment loss to enhance the physical-world transferability of the generated adversarial patches. Comprehensive validation experiments were conducted in both digital and physical domains, for which different classical object detection methods are chosen as the target models. Results demonstrate that the adversarial patch generated by our method achieve effective attacks in digital environments and can be seamlessly transferred to physical scenarios, significantly degrading detection capabilities, where the average attack effectiveness reached 32.99% in the digital domain and 62.52% in the physical domain. This substantiates the practical potential of our proposed framework and future work could extend this methodology to autonomous driving and related fields.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"25024-25038"},"PeriodicalIF":5.3,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11176801","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145255835","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":"Hyperspectral Open-Set Classification via Frequency-Domain Rotation Enhancement and Multibranch Adversarial Routing","authors":"Haibin Wu;Siqi Yan;Chengyang Liu;Aili Wang;Minhui Wang;Liang Yu","doi":"10.1109/JSTARS.2025.3613445","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3613445","url":null,"abstract":"Hyperspectral images have become indispensable for advanced material characterization and environmental monitoring, yet conventional analytical frameworks struggle with the evolving nature of spectral signatures in open-world scenarios. Open-set classification addresses this fundamental limitation by enabling recognition of both known and novel spectral categories during inference. Key technical barriers include rotational invariance in multiangle acquisitions, multiscale feature compatibility across spectral resolutions, frequency-domain discriminative decay, and interference from morphologically similar compounds. To overcome these challenges, we propose a frequency-domain multibranch adversarial routing open-set network integrating four core innovations: fractional Fourier transform layers for rotation-equivariant spectral localization, multibranch dynamic gate routing for uncertainty quantified hierarchical feature fusion, dual-frequency enhancement modules separating diagnostic spectral components through learned frequency gates, and a multiscale adaptive dynamic adversarial spectral mechanism enabling joint spectral–spatial attention refinement. The mathematical codesign of adaptive spectral operators and uncertainty-aware architectures establishes new theoretical foundations for robust open-set analysis in dynamic spectral environments.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"24864-24882"},"PeriodicalIF":5.3,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11176803","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145210021","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}