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

{"title":"Energy-Efficient Deep Learning for Cloud Detection Onboard Nanosatellite","authors":"Imane Khalil;Amina Daghouri;Mohammed Alae Chanoui;Zouhair Guennoun;Adnane Addaim","doi":"10.1109/JSTARS.2025.3553304","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3553304","url":null,"abstract":"Deep learning has been increasingly utilized for cloud detection in Earth observation nanosatellites, offering effective solutions to enhance mission performance. Traditional methods of image capture, onboard storage, and transmission face challenges such as bandwidth limitations and the transmission of cloud-obstructed images, highlighting the need for efficient onboard artificial intelligence. However, energy consumption remains a critical consideration for onboard processing, particularly in nanosatellites where resource constraints are significant. To address these challenges, we propose an optimized SegNet-based deep learning model implemented on the STM32H7 microcontroller for real-time cloud detection, designed to operate within the nanosatellite's strict energy budget. This work, conducted within the project of the UM5-EOSat nanosatellite mission, utilized captured images from the Gecko imager for model evaluation. The customized SegNet architecture, tailored with minimal kernels and layers, achieved an accuracy of 93.50%, effectively balancing performance and computational efficiency. Quantization further optimized energy consumption, achieving a reduction of 82.2% at 280 MHz. The quantized model demonstrated a memory footprint of 304 KB RAM and 110 KB Flash memory, with an inference time of 0.21 s and an energy consumption of 31.41 mJ, ensuring compatibility with the nanosatellite's resource constraints.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"9968-9985"},"PeriodicalIF":4.7,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10935677","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143850908","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":"AfaMamba: Adaptive Feature Aggregation With Visual State Space Model for Remote Sensing Images Semantic Segmentation","authors":"Hongkun Chen;Huilan Luo;Chanjuan Wang","doi":"10.1109/JSTARS.2025.3552942","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3552942","url":null,"abstract":"Remote sensing images semantic segmentation is typically challenging due to the complexity of land cover information. Existing convolutional neural network (CNN)-based models lack the capability to model long-range dependencies, while Transformer-based models are constrained by quadratic computational complexity. Recently, an advanced visual state space model known as the Mamba architecture has been introduced, which ensures linear computational complexity while effectively extracting global contextual information. However, the Mamba architecture lacks the ability to model fine-grained local information, thereby failing to fully leverage both global and local contextual information. To address these issues, we propose a novel network called adaptive feature aggregation with Mamba (AfaMamba). It employs a lightweight ResNet18 as the encoder, and during the decoding phase, it first utilizes a multiscale feature adaptive aggregation module to ensure that the output features from each stage of the encoder contain rich multiscale semantic information. Subsequently, the global-local Mamba structure combines the attention-optimized multiscale convolutional branches with the global branch of Mamba to facilitate effective interaction between global and local features. In addition, a lightweight CNN stem is introduced to extract shallow image features, enhancing the model's ability to capture spatial detail information. Extensive experiments conducted on two widely used remote sensing datasets, ISPRS Potsdam and LoveDA, demonstrate that AfaMamba achieves a superior balance between accuracy and efficiency compared to state-of-the-art models.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"8965-8983"},"PeriodicalIF":4.7,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10933539","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143800844","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":"PSDA: Pyramid Spatial Deformable Aggregation for Building Segmentation in Multiview Remote Sensing Images","authors":"Xuejun Huang;Yi Wan;Yongjun Zhang;Xinyi Liu;Bin Zhang;Yameng Wang;Haoyu Guo;Yingying Pei;Zhonghua Hu","doi":"10.1109/JSTARS.2025.3553030","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3553030","url":null,"abstract":"As increasingly more deep learning models are designed and implemented, the performance of single-view image semantic segmentation is approaching its upper limit. With the increasing availability of multiview satellite images, using multiview information is gaining attention as it can address occlusion problems in single-view images and achieve cross-validation to reduce inappropriate segmentation. However, current multiview semantic segmentation methods often rely on multiview voting or require complex preprocessing steps, which may not fully leverage the advantages of multiview images. We analyzed the complementarity and constraints of multiview information and introduced the pyramid spatial deformable aggregation (PSDA) module, a plug-and-play module designed to enhance multiview feature fusion. PSDA is the core component of our early multiview segmentation framework, which facilitates early-stage information fusion by directly extracting features from multiview images, avoiding the complex and time-consuming production of true orthoimages. In this article, we first show how we created the multiview segmentation dataset (MVSeg dataset) using orthoimages generated from different-view images. Then, the results are shown to prove that our method outperformed the corresponding single-view segmentation method, namely by increasing the intersection over union (IoU) metric by approximately 1.23% –3.68% on both datasets. Due to the fusion of multiview images at an early stage, the computational complexity is 0.29–0.74 times that of the state-of-the-art method, and the IoU metric improved by approximately 2.20% –7.52% on both datasets.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"8995-9008"},"PeriodicalIF":4.7,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10932691","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143800950","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":"Adaptive Token Mixer for Hyperspectral Image Classification","authors":"Shuhan Lei;Meng Zhang;Yuhang Wang;Nan Tang;Ni Jia;Lihua Fu","doi":"10.1109/JSTARS.2025.3552817","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3552817","url":null,"abstract":"MLP-like models have shown strong potential in hyperspectral image (HSI) classification. However, their dense connections among all neurons (tokens) lead to large model sizes, high computational costs, and increased risk of overfitting. To address these issues, researchers have proposed sparse connectivity strategies to create more compact MLP models by selecting and mixing only a subset of tokens. However, most token selection rules overlook image patch content, often introducing task-irrelevant tokens with little valuable class distribution information. This problem is particularly severe in HSIs, which contain rich spatial and spectral information. To overcome this, we propose an adaptive token mixer (ATM) to effectively integrate spatial information in HSIs. ATM adaptively learns token positions based on their content, enabling the model to identify relevant tokens and capture global spatial information across the entire spatial domain. In addition, we introduce a cross-shaped convolutional operator (COSTCO) to enhance local spatial feature extraction. The combination of ATM and COSTCO enables comprehensive token mixing by integrating both global and local spatial information. Experimental results show that this proposed adaptive MLP focuses on the most informative, task-relevant regions during decision-making, offering interpretability to help users understand its predictions. Moreover, the adaptive MLP achieves state-of-the-art performance on HSI classification tasks across four publicly available datasets.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"8882-8896"},"PeriodicalIF":4.7,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10933583","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143800907","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":"Enhanced Ground Fissure Detection in Mining Areas Based on Visible–Infrared Image Fusion and YOLOv5","authors":"Yixin Zhao;Liangchen Zhao;Jihong Guo;Kangning Zhang;Chunwei Ling;Shirui Wang;Hua Bian","doi":"10.1109/JSTARS.2025.3552923","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3552923","url":null,"abstract":"Intensive mining operations can lead to the formation of fissures caused by ground subsidence, which present significant threats to building stability, coal mine safety, and the ecological environment. Accurate and timely detection of these fissures is crucial for effective risk management and mitigation. This article proposes FisFusionYOLO, a novel method that integrates visible–infrared image fusion with a YOLOv5 deep learning network to enhance fissure detection accuracy and efficiency. By combining complementary information from visible and infrared images, the fusion strategy improves the representation of fissure features, which are then processed by the YOLOv5 network for precise and efficient object detection. A dataset collected from the Daliuta Mine in the Shendong Mining Area, using a uncrewed aerial vehicle (UAV) equipped with infrared and visible sensors, demonstrates the effectiveness of the FisFusionYOLO. The method achieves a mean average precision (mAP) score of 82.6%, surpassing those trained on visible and infrared image datasets. Furthermore, FisFusionYOLO exhibits superior generalization performance (77.1% mAP), compared to 24.2% for the visible image detector and 24.2% for the infrared image detector. A statistical analysis of fissure distribution and self-healing properties, based on the detection results, provides valuable insights for proactive risk mitigation. This approach offers a robust, automated solution for monitoring ground fissures in mining areas by integrating advanced image fusion techniques with deep learning. The proposed method can contribute to improved safety practices and environmental protection by enabling early detection and systematic assessment of fissure-related hazards.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"9033-9053"},"PeriodicalIF":4.7,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10933510","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143800905","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":"Bridge Deformation Prediction Using KCC-LSTM With InSAR Time Series Data","authors":"Zechao Bai;Chang Shen;Yanping Wang;Yun Lin;Yang Li;Wenjie Shen","doi":"10.1109/JSTARS.2025.3552665","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3552665","url":null,"abstract":"As a crucial component of the transportation infrastructure, the health of bridge plays a direct role in the traffic safety. Over time, gradual structural deformation can compromise a bridge's stability and safety. Therefore, accurately predicting bridge deformation is essential for analyzing its causes and detecting potential safety hazards in a timely manner. Satellite-based synthetic aperture radar interferometry (InSAR) technology, which detects deformation at millimeter-scale precision over large areas, offers significant advantages in monitoring bridge deformation. However, most existing time-series deformation prediction methods based on InSAR data primarily focus on land subsidence. Given that bridge is complex, singular structures with unique spatial-temporal characteristics, existing methods designed for land subsidence are not directly applicable to bridge deformation prediction. To address this challenge, we propose a novel K-shape and complete linkage hierarchical cluster long short-term memory (KCC-LSTM) approach for predicting bridge deformation based on time-series InSAR data. The approach initially combines two machine learning based clustering algorithms, K-Shape for better capturing shape features of time series and complete linkage hierarchical clustering combined with spatial geographic location captures the spatial characteristics of time series to derive clusters with unique spatiotemporal deformation behavior, improving clustering accuracy and spatiotemporal correlation. Clustering results generated from this unsupervised machine learning approach are later used as training labels to develop long short-term memory (LSTM) networks. We validate the proposed approach using time-series data from 100 X-band TerraSAR-X images, acquired from 13 April 2010 to 13 December 2019. Our results demonstrate that compared to standard LSTM, the proposed approach reduces root mean square error of Bridge 1 from 3.6 to 0.5 mm and Bridge 2 from 3.6 to 1.3 mm, improving prediction accuracy. The results underscore the effectiveness of the KCC-LSTM model in predicting deformation in complex infrastructure, such as bridge.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"9582-9592"},"PeriodicalIF":4.7,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10930833","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143835431","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":"Enhancing Aerosol Vertical Distribution Retrieval With Combined LSTM and Transformer Model From OCO-2 O2 A-Band Observations","authors":"YuXuan Wang;RuFang Ti;ZhenHai Liu;Xiao Liu;HaiXiao Yu;YiChen Wei;YiZhe Fan;YuYao Wang;HongLian Huang;XiaoBing Sun","doi":"10.1109/JSTARS.2025.3552310","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3552310","url":null,"abstract":"The precise determination of aerosol vertical distribution is crucial for accurate radiative transfer simulations in atmospheric aerosol studies. This research utilizes Orbiting Carbon Observatory-2 oxygen A-band hyperspectral observation data, which are sensitive to aerosol vertical distribution. We propose a novel machine learning model that combines long short-term memory and Transformer architectures. Furthermore, a physics-based, information-driven band selection method was developed to simplify input data and reduce complexity. To enhance the algorithm's applicability, the model was applied across the entire African continent and adjacent water bodies. For multiple dust events in West Africa, the retrieved aerosol layer height (ALH) and aerosol optical depth (AOD) values exhibit strong agreement with the cloud–aerosol Lidar with orthogonal polarization, yielding the correlation coefficients of 0.6893 for AOD and 0.7866 for ALH. The model's high retrieval accuracy is validated using two metrics: Earth mover's distance and mean-squared error. By integrating advanced machine learning techniques into remote sensing, this study achieves a significant improvement in retrieval accuracy over previous methods. Through systematic optimization, the model provides a robust solution for accurately characterizing aerosol layers, making it a valuable tool for advancing atmospheric aerosol research.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"9650-9665"},"PeriodicalIF":4.7,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10930835","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143835441","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":"Joint Spectral Information and Spatial Details for Road Extraction From Optical Remote-Sensing Images","authors":"Yuzhun Lin;Jie Rui;Fei Jin;Shuxiang Wang;Xibing Zuo;Xiao Liu","doi":"10.1109/JSTARS.2025.3552587","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3552587","url":null,"abstract":"Currently, satellite remote-sensing image acquisition systems typically include two forms of panchromatic and multispectral images, both of which have complementary advantages in spatial and channel dimensions. However, translating advantageous information into a deciphering function in road-extraction tasks remains a challenge. This study, therefore, proposes a road-extraction method combining spectral information and spatial details. First, a multibranch network framework was built based on an encoding–decoding structure. The encoding layers of the panchromatic and multispectral image branches were constructed from the residual modules. Fusion branches were then constructed during the decoding phase. The spectral information of the multispectral image and spatial details of the panchromatic image were then obtained using the HIS color transform and Haar wavelet transform, respectively, and injected into the fusion branch. A polarized self-attention mechanism was finally introduced at different levels of the fusion branch to reduce information loss during feature extraction, and operations, such as connected convolution and nonlinear activation, were later connected to complete the road prediction. The implementation of the proposed method on the GF2-FC and CHN6-CUG datasets revealed a superior performance compared with comparative methods in terms of quantitative evaluation metrics. The proposed method performed the strongest in several scenarios, particularly in difficult road-extraction areas, such as shadows and vegetation cover.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"9723-9737"},"PeriodicalIF":4.7,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10930777","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143835456","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":"Power Line Aerial Image Restoration Under Adverse Weather: Datasets and Baselines","authors":"Sai Yang;Bin Hu;Bojun Zhou;Fan Liu;Xiaoxin Wu;Xinsong Zhang;Juping Gu;Jun Zhou","doi":"10.1109/JSTARS.2025.3552582","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3552582","url":null,"abstract":"Power line autonomous inspection (PLAI) plays a crucial role in the construction of smart grids due to its great advantages of low cost, high efficiency, and safe operation. PLAI is completed by accurately detecting the electrical components and defects in the aerial images captured by unmanned aerial vehicles. However, the visible quality of aerial images is inevitably degraded by adverse weather, haze, rain, or snow, which are found to drastically decrease the detection accuracy in our research. To circumvent this problem, we propose a new task of power line aerial image restoration under adverse weather (PLAIR-AW), which aims to recover clean and high-quality images from degraded images with bad weather thus improving detection performance for PLAI. In this context, we are the first to release numerous corresponding datasets, namely, HazeCPLID, HazeTTPLA, HazeInsPLAD for power line aerial image dehazing, RainCPLID, RainTTPLA, RainInsPLAD for power line aerial image deraining, SnowCPLID, SnowInsPLAD for power line aerial image desnowing, which are synthesized upon the public power line aerial image datasets of CPLID, TTPLA, InsPLAD following the mathematical models. Meanwhile, we select numerous state-of-the-art methods from image restoration community as the baseline methods for PLAIR-AW. At last, we conduct large-scale empirical experiments to evaluate the performance of baseline methods on the proposed datasets.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"10105-10119"},"PeriodicalIF":4.7,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10930826","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143850846","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":"Lossless Compression Framework Using Lossy Prior for High-Resolution Remote Sensing Images","authors":"Enjia Gu;Yongshan Zhang;Xinxin Wang;Xinwei Jiang","doi":"10.1109/JSTARS.2025.3550721","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3550721","url":null,"abstract":"Lossless compression of remote sensing images is critically important for minimizing storage requirements while preserving the complete integrity of the data. The main challenge in lossless compression lies in striking a good balance between reasonable compression durations and high compression ratios. In this article, we introduce an innovative lossless compression framework that uniquely utilizes lossy compression data as prior knowledge to enhance the compression process. Our framework employs a checkerboard segmentation technique to divides the original remote sensing image into various subimages. The main diagonal subimages are compressed using a traditional lossy method to obtain prior knowledge for facilitating the compression of all subimages. These subimages are then subjected to lossless compression using our newly developed lossy prior probability prediction network (LP3Net) and arithmetic coding in a specific order. The proposed LP3Net is an advanced network architecture, consisting of an image preprocessing module, a channel enhancement module, and a pixel probability transformer module, to learn the discrete probability distribution of each pixel within every subimage, enhancing the accuracy and efficiency of the compression process. Experiments on high-resolution remote sensing image datasets demonstrate the effectiveness and efficiency of the proposed LP3Net and lossless compression framework, achieving a minimum of 4.57% improvement over traditional compression methods and 1.86% improvement over deep learning-based compression methods.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"8590-8601"},"PeriodicalIF":4.7,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10930824","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761388","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}