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

{"title":"Fusion-Decomposition Pan-Sharpening Network With Interactive Learning of Representation Graph","authors":"Yunxuan Tang;Xue Wang;Peng Liu;Tong Li","doi":"10.1109/JSTARS.2024.3524386","DOIUrl":"https://doi.org/10.1109/JSTARS.2024.3524386","url":null,"abstract":"Deep learning (DL)-based pan-sharpening methods have become mainstream due to their exceptional performance. However, the lack of ground truth for supervised learning forces most DL-based methods to use pseudoground truth multispectral images, limiting learning potential and constraining the model's solution space. Unsupervised methods often neglect mutual learning across modalities, leading to insufficient spatial details in pan-sharpened images. To address these issues, this study proposes a fusion-decomposition pan-sharpening model based on interactive learning of representation graphs. This model considers both the compression process from source images to fused results and the decomposition process back to the source images. It aims to leveraging feature consistency between these processes to enhance the spatial and spectral consistency learned by the fusion network in a data-driven manner. Specifically, the fusion network incorporates the meticulously designed representational graph interaction module and the graph interaction fusion module. These modules construct a representational graph structure for cross-modal feature communication, generating a global representation that guides the cross-modal semantic aggregation of multispectral and panchromatic data. In the decomposition network, the spatial structure perception module and the spectral feature extraction module, designed based on the attributes of the source image features, enable the network to better perceive and reconstruct multispectral and panchromatic data from the fused result. This, in turn, enhances the fusion network's perception of spectral information and spatial structure. Qualitative and quantitative results on the IKONOS, GaoFen-2, WorldView-2, and WorldView-3 datasets validate the effectiveness of the proposed method in comparison to other state-of-the-art methods.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"3812-3826"},"PeriodicalIF":4.7,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10818769","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143105989","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":"Multisensor Data Fusion and GIS-DRASTIC Integration for Groundwater Vulnerability Assessment With Rainfall Consideration","authors":"Wu Jiazhe;Dai Xinrui;Su Yancheng;Zheng Xiangtian;Bushra Ghaffar;Rabiya Nasir;Ahsan Jamil;Zeeshan Zafar;Mohammad Suhail Meer;M. Abdullah-Al-Wadud;Rahila Naseer;Hesham El-Askary","doi":"10.1109/JSTARS.2024.3524376","DOIUrl":"https://doi.org/10.1109/JSTARS.2024.3524376","url":null,"abstract":"In many areas of the world, particularly in arid and semi-arid regions, groundwater is the primary source of fresh water, and it supplies around one-third of the world's fresh water. Agriculture is the primary economic sector on the coast in the southern district (Nowshera). More food productivity is required due to the expanding population and diminishing agricultural lands, which increases the use of chemical pesticides and fertilizers in farming. The current study was conducted in northwestern parts of Pakistan to evaluate the impacts of the frequent use of pesticides and fertilizers in agricultural fields. Nine hydrogeological parameters were considered, and the GIS-based DRASTIC index was used to generate the final groundwater vulnerability map. The index map (ranging from 220 to 1980) was further classified into five classes based on index vulnerability: very low (220–345), low (346–670), moderate (671–730), high (731–1239), and very high (1240–1980). Nitrate and TDS, the two reliable and recognized scientific water quality measurements, have been used to validate the model. By regulating and controlling anthropogenic and agricultural pollution, the danger of contamination can be decreased. This research will aid in understanding the possible dangers and risks related to the usage of pesticides in agriculture and other industries. Furthermore, it will help identify the specific pesticides causing the contamination, assess the extent and severity of the contamination, and develop strategies to protect public health and the environment.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"3556-3568"},"PeriodicalIF":4.7,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10818750","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106106","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 Hierarchical Local-Sparse Model for Semantic Change Detection in Remote Sensing Imagery","authors":"Fachuan He;Hao Chen;Shuting Yang;Zhixiang Guo","doi":"10.1109/JSTARS.2024.3522910","DOIUrl":"https://doi.org/10.1109/JSTARS.2024.3522910","url":null,"abstract":"In response to the existing challenges in semantic change detection (SCD) for remote sensing images, such as weak spatiotemporal correlation and insufficient utilization of local neighborhood information, this article proposes a SCD network based on hierarchical local-sparse attention (HLSNet). The network combines a fully convolutional network with a deep transformer structure to leverage the advantages of local feature extraction and long-range information connection. Next, a hierarchical local-sparse attention is proposed to exploit the neighborhood characteristics of target pixels using a dual-window attention mechanism, the aim is to increase the receptive field while minimizing the interference of redundant information. By focusing on all tokens within a smaller window and dynamically selecting key tokens within a larger window for attention calculation, this two-tiered attention approach allows the model to handle details while capturing broader contextual information. The small window provides tightly related local information, while the larger window offers relevant but potentially more distant information, achieving a hierarchical processing of information from local to long-range. In order to facilitate more comprehensive interaction between the features of pre- and postchange images, each transformer block in the network employs a strategy of concatenating self-attention and cross attention. This approach better captures the spatiotemporal correlations and feature integration, thus achieving efficient and precise change detection. HLSNet achieves the highest accuracy on the two commonly used SCD datasets, SECOND, and Landsat-SCD, with <inline-formula><tex-math>${{F}_{text {scd}}}$</tex-math></inline-formula> values reaching 62.53% and 91.67%, respectively.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"3144-3159"},"PeriodicalIF":4.7,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10818768","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993292","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":"Multiscale Attention Feature Fusion Based on Improved Transformer for Hyperspectral Image and LiDAR Data Classification","authors":"Aili Wang;Guilong Lei;Shiyu Dai;Haibin Wu;Yuji Iwahori","doi":"10.1109/JSTARS.2024.3524443","DOIUrl":"https://doi.org/10.1109/JSTARS.2024.3524443","url":null,"abstract":"With the uninterrupted evolution of remote sensing data, the list of available data sources has expanded, effectively utilizing useful information from multiple sources for better land surface observation, which has become an intriguing and challenging problem. However, the complexity of urban areas and their surrounding structures makes it extremely difficult to capture correlations between features. This article proposes a novel multiscale attention feature fusion network, composed of hierarchical convolutional neural networks and transformer to enhance joint classification accuracy of hyperspectral image (HSI) and light detection and ranging (LiDAR) data. First, a multiscale fusion Swin transformer module is employed to eliminate information loss in feature propagation, which explores deep spatial–spectral features of HSI while extracting height information from LiDAR data. This structure combines the advantages of the Swin transformer, featuring a nonlocal receptive field fusion by progressively expanding the window's receptive field layer by layer while preserving the spatial features of the image. It also exhibits excellent robustness against spatial misalignment. For the dual branches of hyperspectral and LiDAR, a dual-source feature interactor is designed, which facilitates interaction between hyperspectral and LiDAR features by establishing a dynamic attention mechanism, which effectively captures correlated information between the two modalities and fuses it into a unified feature representation. The efficacy of the proposed approach is validated using three standard datasets (Huston2013, Trento, and MUUFL) in the experiments. The classification results indicate that the proposed framework, by fully utilizing spatial context information and effectively integrating feature information, significantly outperforms state-of-the-art classification methods.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"4124-4140"},"PeriodicalIF":4.7,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10818716","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143105951","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 Novel Change Detection Method Based on Visual Language From High-Resolution Remote Sensing Images","authors":"Junlong Qiu;Wei Liu;Hui Zhang;Erzhu Li;Lianpeng Zhang;Xing Li","doi":"10.1109/JSTARS.2024.3524382","DOIUrl":"https://doi.org/10.1109/JSTARS.2024.3524382","url":null,"abstract":"Recently, the release of “all-in-one” foundation models has sparked rapid developments in artificial intelligence. However, due to the fact that these models are typically trained on natural images, their potential in remote sensing remains largely untapped. To address this gap, this article proposes a novel change detection method based on visual language from high-resolution remote sensing images, named VLCD. Specifically, on the text side, we use context optimization to align text–image semantics. On the image side, we construct a side fusion network, which integrates universal features from the foundation model with domain-specific features from remote sensing through a bridging module. In addition, we introduce a change feature computation module to integrate global features, difference features, and textual information. To validate the effectiveness of the proposed method, we conducted comparative experiments on three public datasets. The results show that the proposed VLCD achieved state-of-the-art <italic>F</i>1-scores and IoUs on these three datasets: LEVIR-CD (90.99%, 83.46%), SYSU-CD (83.05%, 71.01%), and S2Looking (62.75%, 45.89%), outperforming the results obtained through full fine-tuning while using less than one-tenth of the number of parameters.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"4554-4567"},"PeriodicalIF":4.7,"publicationDate":"2024-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10818767","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106042","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":"New Insights Into the Reservoir Landslide Deformation Mechanism From InSAR and Numerical Simulation Technology","authors":"Guoshi Liu;Bin Wang;Qian Sun;Jun Hu;Lei-Lei Liu;Wanji Zheng;Liye Zou","doi":"10.1109/JSTARS.2024.3523294","DOIUrl":"https://doi.org/10.1109/JSTARS.2024.3523294","url":null,"abstract":"Reservoir landslides represent a significant geological hazard that jeopardizes the safety of reservoirs. Deformation monitoring and numerical simulation are essential methodologies for elucidating the evolutionary patterns of landslides. Nonetheless, the existing approaches exhibit limitations in revealing the potential deformation mechanism. Consequently, this study proposes an innovative strategy that incorporates interferometric synthetic aperture radar (InSAR) deformation characteristics alongside fluid–solid coupling stress analysis to investigate the deformation, focusing on the Shuizhuyuan landslide within the Three Gorges Reservoir area as a case study. Using temporary coherence point InSAR technology, significant motion units were identified, with a maximum deformation rate of −60 mm/yr. The complete deformation time series reveals three independent components of landslide movement and their trigger factors geometrically. Subsequently, the saturation permeability coefficient of the sliding mass in the seepage analysis is modified with the assistance of InSAR deformation. Then, we coupled the seepage analysis results to FLAC3D model for stress and strain analysis, and determined the seepage-induced progressive failure mechanism and the deformation mode of the Shuizhuyuan landslide, driven by reservoir water-level (RWL) drop. The numerical simulation results aid in interpreting the deformation mechanism of different spatial and temporal patterns of landslides from three aspects: hydrodynamic pressure from rainfall infiltration, groundwater hysteresis caused by RWL drop, and seepage forces from RWL rise. Furthermore, our findings reveal that the dynamic factor of safety (FOS) of landslide during the InSAR observation period is highly consistent with the periodic fluctuations of the RWL. However, there is also a small trend of overall decline in FOS that cannot be ignored.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"2908-2927"},"PeriodicalIF":4.7,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10817503","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993381","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":"$mathrm{D}^{3}$T: Deep Denoising Dictionary Tensor for Hyperspectral Anomaly Detection","authors":"Qiangqiang Shen;Zonglin Liu;Hanzhang Wang;Yanhui Xu;Yongyong Chen;Yongsheng Liang","doi":"10.1109/JSTARS.2024.3523133","DOIUrl":"https://doi.org/10.1109/JSTARS.2024.3523133","url":null,"abstract":"Hyperspectral anomaly detection (HAD) approaches with tensor low-rank representation (TLRR) have shown engaging performance, which are capable of capturing abundant spectral and spatial information and, hence, extracting sparse anomalies from the low-rank background without ruining the intrinsic geometric structures of tensor data. However, existing HAD approaches construct the low-rank representation by a handcrafted dictionary that still contains the anomalies, resulting in an inferior representation. To this end, we propose a deep denoising dictionary tensor for hyperspectral anomaly detection (<inline-formula><tex-math>$mathrm{D}^{3}$</tex-math></inline-formula>T), which can balance performance and interpretability by fusing two intuitive priors, i.e., low-rankness and deep denoising, into the dictionary tensor and coefficient tensor. In particular, the proposed <inline-formula><tex-math>$mathrm{D}^{3}$</tex-math></inline-formula>T first designs a tensor recovery module for dividing the low-rank background and sparse anomalies, where the background is optimized as a denoising dictionary tensor by the joint low-rankness and deep denoising. Following that, we build the TLRR model based on the denoising dictionary tensor to explore the latent representation of the input hyperspectral images, and hence, each background pixel is represented by the other clear background pixels in place of the anomaly pixels. Meanwhile, the coefficient tensor in TLRR is also optimized by the two priors to fully explore the spatial and spectral correlations in the background, and hence, the anomalies can be accurately detected. Numerous experimental results from various real-world datasets validate the effectiveness of our <inline-formula><tex-math>$mathrm{D}^{3}$</tex-math></inline-formula>T.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"3713-3727"},"PeriodicalIF":4.7,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10818590","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106181","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":"Dual Embedding Transformer Network for Hyperspectral Unmixing","authors":"Huadong Yang;Chengbi Zhang","doi":"10.1109/JSTARS.2024.3523747","DOIUrl":"https://doi.org/10.1109/JSTARS.2024.3523747","url":null,"abstract":"Hyperspectral unmixing is an essential task for achieving accurate perception of hyperspectral remote sensing information, aiming to overcome the limitation of spatial resolution and interpret the distribution of land features. To achieve the spatial and spectral feature representation of hyperspectral images, we propose a dual embedding transformer network (DET-Net) based on an encoder-decoder architecture, which utilizes two transformer modules, including three-view spatial attention (TVA) module with 2-D embedding and multiscale spectral band group feature fusion (BGF) module with 3-D embedding to accomplish the task of hyperspectral unmixing. In TVA module, based on 2-D embedding, we introduce a three-view attention mechanism to extract more comprehensive spatial features. In BGF module, the transformer embedding is extended to band group spatial-spectral 3-D cubed embedding and establishes a series of spectral band groups. A cross-feature fusion mechanism is adopted to achieve multiscale spatial-spectral feature decoupling. With the collaboration of these two embeddings, DET-Net effectively captures complex spatial and spectral dependencies to decouple the tridimensional unmixing feature representation. Experimental results on synthetic and real datasets demonstrates the generalization performance of the proposed method, and the ablation experiments confirm the effectiveness of the TVA and BGF modules.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"3514-3529"},"PeriodicalIF":4.7,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10818529","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106034","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":"KE-RSIC: Remote Sensing Image Captioning Based on Knowledge Embedding","authors":"Kangda Cheng;Erik Cambria;Jinlong Liu;Yushi Chen;Zhilu Wu","doi":"10.1109/JSTARS.2024.3523944","DOIUrl":"https://doi.org/10.1109/JSTARS.2024.3523944","url":null,"abstract":"Current remote sensing image captioning methods often struggle to provide accurate and comprehensive descriptions due to their reliance on networks designed for natural images. Due to limited domain-specific knowledge in remote sensing, these networks often fail to accurately reflect the intrinsic semantic information of remote sensing categories. This article proposes a novel knowledge-embedded remote sensing image captioning model. We first define two types of remote sensing knowledge: general knowledge within the field of remote sensing, and specific knowledge that is relevant to the input image. To acquire general knowledge, we construct a remote sensing knowledge graph and propose a general knowledge embedding method, enabling semantic correlations between entities and relationships in remote sensing knowledge graphs. The generated entity embeddings and relationship embeddings can effectively capture the intrinsic semantic information of remote sensing categories. To acquire specific knowledge, we also propose a specific knowledge embedding method. We retrieve reports with similar label distributions to the input and then extract entities and relationships from the retrieved reports using a relation extractor. Embedding specific knowledge can alleviate to some extent the issue of poor matching between visual features and semantic features due to the lack of relevant knowledge. Subsequently, to integrate entity embeddings, relationship embeddings, and visual features, we propose a visual feature and knowledge information dynamic fusion module. This module can efficiently combine the visual features of remote sensing images with structural information on embedded knowledge. Numerous experimental findings attest to the superiority and effectiveness of the proposed method.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"4286-4304"},"PeriodicalIF":4.7,"publicationDate":"2024-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10818406","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106072","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":"Infrared Image Enhancement: A Review","authors":"Qingwang Wang;Pengcheng Jin;Yuhang Wu;Liyao Zhou;Tao Shen","doi":"10.1109/JSTARS.2024.3523418","DOIUrl":"https://doi.org/10.1109/JSTARS.2024.3523418","url":null,"abstract":"Under special conditions such as night, rain, and fog, visible light imaging technology often performs poorly, while radar and other imaging technologies are limited due to their high costs. Infrared imaging technology, based on the principle of thermal radiation, can provide clear imaging effects under these extreme conditions and has a relatively low cost of use. Therefore, it has been widely applied in various fields, including military, medical, industrial, and agricultural applications. However, due to the limitations of infrared wavelengths and imaging technology, traditional infrared imaging devices struggle to capture rich texture information, leading to infrared images that lack texture details and have low resolution, which significantly restricts the further research and application of infrared imaging technology in various fields. In recent years, with the widespread attention to infrared imaging technology, researchers have proposed various new infrared image enhancement techniques. Despite this, the lack of texture information in ordinary infrared images leads to the enhancement effect of existing technologies being unsatisfactory. Therefore, we have conducted a systematic investigation of the research advancements in the field of infrared image enhancement, encompassing infrared image enhancement methods, related datasets, and evaluation metrics, with the aim of exploring a research solution that could potentially break through current technological limitations. Based on these investigations, we have evaluated the performance of various representative infrared image enhancement methods, with the hope of providing a reference for future research. In addition, this article also provides a comprehensive introduction to the potential applications of infrared image enhancement technology and discusses significant research directions for the future.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"3281-3299"},"PeriodicalIF":4.7,"publicationDate":"2024-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10817543","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993457","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}