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

{"title":"All-Weather Retrieval of Total Column Water Vapor From Aura OMI Visible Observations","authors":"Jiafei Xu;Zhizhao Liu","doi":"10.1109/JSTARS.2024.3523048","DOIUrl":"https://doi.org/10.1109/JSTARS.2024.3523048","url":null,"abstract":"Total column water vapor (TCWV), retrieved from satellite remotely sensed measurements, plays a critically important role in monitoring Earth's weather and climate. The ozone monitoring instrument (OMI) can obtain daily near-global TCWV observations using the visible spectra. The observational accuracy of OMI-estimated TCWV under cloudy-sky conditions is much poorer than OMI-measured clear-sky TCWV. Satellite-based OMI-derived TCWV data, observed with little cloud contamination, are solely used, which, in general, are limited and discontinuous observations. We propose a practical machine learning-based TCWV retrieval algorithm to derive TCWV over land from OMI visible observations under all weather conditions, considering multiple dependable factors linked with OMI TCWV and air mass factor. The global TCWV data, observed from 6000 global navigation satellite system (GNSS)-based training stations in 2017, are utilized as the expected TCWV estimates in the algorithm training process. The retrieval approach is validated in 2018–2020 across the world using ground-based TCWV from additional 4,465 GNSS-based verification stations and 783 radiosonde-based verification stations. The newly retrieved TCWV estimates remarkably outperform operational OMI-retrieved water vapor data, regardless of cloud fraction and TCWV levels. In terms of root-mean-square error, it is overall reduced by 90.44% from 56.38 to 5.39 mm and 90.19% from 53.23 to 5.22 mm compared with GNSS and radiosonde TCWV, respectively. The retrieval algorithm stays stable, both temporally and spatially. This research provides a valuable technique to precisely retrieve OMI-based TCWV data records under all weather conditions, which could be applicable to other satellite-borne visible sensors like GOME-2, SCIAMACHY, and TROPOMI.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"3057-3070"},"PeriodicalIF":4.7,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10816453","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993480","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":"STeInFormer: Spatial–Temporal Interaction Transformer Architecture for Remote Sensing Change Detection","authors":"Xiaowen Ma;Zhenkai Wu;Mengting Ma;Mengjiao Zhao;Fan Yang;Zhenhong Du;Wei Zhang","doi":"10.1109/JSTARS.2024.3522329","DOIUrl":"https://doi.org/10.1109/JSTARS.2024.3522329","url":null,"abstract":"Convolutional neural networks and attention mechanisms have greatly benefited remote sensing change detection (RSCD) because of their outstanding discriminative ability. Existent RSCD methods often follow a paradigm of using a noninteractive Siamese neural network for multitemporal feature extraction and change detection heads for feature fusion and change representation. However, this paradigm lacks the contemplation of the characteristics of RSCD in temporal and spatial dimensions, and causes the drawback on spatial–temporal interaction that hinders high-quality feature extraction. To address this problem, we present a spatial–temporal interaction Transformer architecture for multitemporal feature extraction, which is the first general backbone network specifically designed for RSCD. In addition, we propose a parameter-free multifrequency token mixer to integrate frequency-domain features that provide spectral information for RSCD. Experimental results on three datasets validate the effectiveness of the proposed method, which can outperform the state-of-the-art methods and achieve the most satisfactory efficiency-accuracy tradeoff.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"3735-3745"},"PeriodicalIF":4.7,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10815617","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143105952","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":"Unified Dynamic Dictionary and Projection Optimization With Full-Rank Representation for Hyperspectral Anomaly Detection","authors":"Hongran Li;Chao Wei;Yizhou Yang;Zhaoman Zhong;Ming Xu;Dongqing Yuan","doi":"10.1109/JSTARS.2024.3522388","DOIUrl":"https://doi.org/10.1109/JSTARS.2024.3522388","url":null,"abstract":"Hyperspectral anomaly detection (HAD) aims to classify each pixel in a hyperspectral image as either background or anomaly without requiring labeled data. Traditional reconstruction based methods model the background using a predefined static background dictionary and low-rank representation coefficients. However, when anomalies are present, the use of a static dictionary can lead to inaccurate background representation, which is easily disturbed by anomalous points. Moreover, existing methods typically focus on the low-rank and smooth characteristics of the background during reconstruction, overlooking deeper features of the background representation. This motivates us to reconsider how the background should be represented. To address these issues, we propose an innovative HAD method that integrates background dictionary learning into the anomaly decomposition process. By using projection operators to optimize the background dictionary, we overcome the limitations of traditional methods that rely on static dictionaries. In addition, we revisit the representation of the background and emphasize the importance of applying nonnegative full-rank constraint to the representation coefficients under the new background dictionary. These improvements result in a more accurate background representation, thereby enhancing anomaly detection performance. Experimental results on several hyperspectral datasets demonstrate that the proposed algorithm excels in anomaly detection tasks, offering new insights and approaches for HAD.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"4032-4049"},"PeriodicalIF":4.7,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10815627","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106069","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":"GFTT: Geographical Feature Tokenization Transformer for SAR-to-Optical Image Translation","authors":"Hongbo Liang;Xuezhi Yang;Xiangyu Yang;Jinjin Luo;Jiajia Zhu","doi":"10.1109/JSTARS.2024.3523274","DOIUrl":"https://doi.org/10.1109/JSTARS.2024.3523274","url":null,"abstract":"Synthetic aperture radar (SAR) image to optical image translation not only assists information interpretability, but also fills the gaps in optical applications due to weather and light limitations. However, several studies have pointed out that specialized methods heavily struggle to deliver images with widely varying optical imaging styles, thus, resulting in poor image translation with disharmonious and repetitive artifacts. Another critical issue attributes to the scarcity of geographical prior knowledge. The generator always attempts to produce images within a narrow scope of the data space, which severely restricts the semantic correspondence between SAR content and optical styles. In this article, we introduce a novel tokenization, namely geographical imaging tokenizer (GIT), which captures imaging style of ground materials in the optical image. Based on the GIT, we propose a geographical feature tokenization transformer framework (GFTT) that discovers the consensus between SAR and optical images. In addition, we leverage a self-supervisory task to encourage the transformer to learn meaningful semantic correspondence from local and global style patterns. Finally, we utilize the noise-contrastive estimation loss to maximize mutual information between the input and translated image. Through qualitative and quantitative experimental evaluations, we verify the reliability of the proposed GIT that aligns with authentic expressions of the optical observation scenario, and indicates the superiority of GFTT in contrast to the state-of-the-art algorithms.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"2975-2989"},"PeriodicalIF":4.7,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10816574","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993383","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 Susceptibility and Risk of Land Subsidence by Integrating InSAR and Hybrid Machine Learning Models: A Case Study in Xi'an, China","authors":"Chen Chen;Mimi Peng;Mahdi Motagh;Xinxin Guo;Mengdao Xing;Yinghui Quan","doi":"10.1109/JSTARS.2024.3522995","DOIUrl":"https://doi.org/10.1109/JSTARS.2024.3522995","url":null,"abstract":"Land subsidence is a widespread geo-hazard, and it can be effectively monitored with the Interferometric Synthetic Aperture Radar (InSAR) technique. Assessing land subsidence plays a significant role in ensuring safety and enhancing disaster prevention. It requires not only focusing on the extent or rate of deformation but also evaluating the susceptibility and risk of subsidence. In this article, we propose a new comprehensive subsidence susceptibility and risk assessment strategy by integrating InSAR observation and hybrid machine learning models, which is first and successfully employed over Xi'an area. In this study, four machine learning models are compared to determine the optimal model, and found that the Random Forest (RF) performs the best in predicting InSAR-derived spatial deformation (Root Mean Square Error = 3.53 mm) and susceptibility (Area Under the Curve = 0.97). Also, the authenticity and reliability of the susceptibility results from RF model are improved by further Isotonic regression calibration processes. Then, subsidence risk map is obtained from the hazard and vulnerability assessment using the Analytic Hierarchy Process by combining multiple conditional factors. Remarkably, the results revealed that regions with the very high and high risk level of subsidence are 12.33 <inline-formula><tex-math>$text{km}^{2}$</tex-math></inline-formula> and correspond to 1.34<inline-formula><tex-math>$%$</tex-math></inline-formula> of the total. It further found that the groundwater level and its changes are the domain factors in Xi'an from machine learning method. This work provides an integrated assessment approach for subsidence from a new perspective, and the findings can serve as theoretical support for early warning and disaster prevention.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"3625-3639"},"PeriodicalIF":4.7,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10816455","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143105430","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":"UAV Hyperspectral Remote Sensing Image Classification: A Systematic Review","authors":"Zhen Zhang;Lehao Huang;Qingwang Wang;Linhuan Jiang;Yemao Qi;Shunyuan Wang;Tao Shen;Bo-Hui Tang;Yanfeng Gu","doi":"10.1109/JSTARS.2024.3522318","DOIUrl":"https://doi.org/10.1109/JSTARS.2024.3522318","url":null,"abstract":"In recent years, significant advances in unmanned aerial vehicle (UAV) technology and hyperspectral remote sensing have spurred rapid and innovative developments in UAV-based hyperspectral image (HSI) classification across a range of fields, including environmental monitoring, precision agriculture, forest health assessment, and disaster management. Compared to spaceborne platforms, the spectra of ground objects observed by UAV platforms exhibit notable variations, presenting more pronounced challenges for accurate classification. This article provides an in-depth and systematic review of UAV HSI classification techniques, systematically examining the evolution from traditional machine learning approaches, such as sparse coding, compressed sensing, and kernel methods, to cutting-edge deep learning frameworks, including convolutional neural networks, Transformer models, recurrent neural networks, graph convolutional networks, generative adversarial networks, and hybrid models. Although traditional methods demonstrate effectiveness in certain scenarios, their limitations become increasingly apparent when dealing with high-dimensional, nonlinear spectral data. In contrast, deep learning-based models excel at capturing intricate relationships between spectral and spatial features, significantly boosting classification accuracy and emerging as the dominant paradigm in the field. The WHU-Hi hyperspectral remote sensing dataset is utilized as a case study to elucidate the advantages and limitations of various deep learning methods through rigorous qualitative and quantitative comparisons. The potential of UAV hyperspectral image classification techniques in addressing high-dimensional data and complex scenarios is also thoroughly described. Furthermore, this article delves into cutting-edge research trends, such as lightweight model development, hyperspectral large models, multisource data fusion, and model interpretability, while also highlighting future trends for UAV hyperspectral remote sensing classification technology, particularly in real-time monitoring and intelligent applications.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"3099-3124"},"PeriodicalIF":4.7,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10815625","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993376","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":"Comparative Analysis of Learning-Based Approaches for Change Detection in Satellite Images","authors":"Maria-Eirini Pegia;Björn Þór Jónsson;Anastasia Moumtzidou;Ilias Gialampoukidis;Stefanos Vrochidis;Ioannis Kompatsiaris","doi":"10.1109/JSTARS.2024.3522350","DOIUrl":"https://doi.org/10.1109/JSTARS.2024.3522350","url":null,"abstract":"Satellite image change detection, where two images of the same area from different times are compared, is crucial for earth sensing and monitoring applications. Many learning-based detection methods have been proposed for this task, with different performance characteristics. Since these detection methods have been tested under different settings, comparing their performance across a variety of situations is difficult. The goal of this article is therefore to comprehensively compare the state-of-the-art detection methods from the literature, across a variety of dataset parameters. To that end, we analyze the impact of image resolution, training set size, and noise on learning performance. A first set of experiments, using a large set of high-resolution images, reveals that training set resolution should match the resolution of the images the model will be applied to, that larger training sets are beneficial, and that adding Gaussian noise improves performance. A second set of experiments, using a smaller set of low-resolution images, confirms that the training set should also be of the same low resolution, but shows that adding noise does not improve performance in this case. The results also indicate that BiasUNet is the most effective method for detecting changes between image pairs.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"3766-3781"},"PeriodicalIF":4.7,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10815622","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106044","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 Strategy Based Improved Swarm Intelligence and Stacked LSTM With Residual Connection for Land Use Land Cover Classification","authors":"Vinaykumar Vajjanakurike Nagaraju;Ananda Babu Jayachandra;Andrzej Stateczny;Swathi Holalu Yogesh;Raviprakash Madenur Lingaraju;Balaji Prabhu Baluvaneralu Veeranna","doi":"10.1109/JSTARS.2024.3522197","DOIUrl":"https://doi.org/10.1109/JSTARS.2024.3522197","url":null,"abstract":"Land use land cover (LULC) classification using satellite images is crucial for land-use inventories and environment modeling. The LULC classification is a difficult task because of the high dimensional feature space, which affects the classification accuracy. This article proposes a dual strategy-based bald eagle search (DSBES) algorithm and stacked long short-term memory (LSTM) with residual connection for LULC classification. The dual strategy includes adaptive inertia weight and phasor operator strategy to select relevant features from the feature subset. The stacked LSTM contains multiple layers stacked on top of each other to capture high-level temporal data. By integrating residual connection with stacked LSTM, gradient flow is enabled directly among long sequences, reducing the vanishing gradient issue and fastening the convergence rate. The DSBES and stacked LSTM with residual connection performance are examined in terms of metrics of accuracy, precision, sensitivity, specificity, f1-score, and computational time. The DSBES and stacked LSTM with residual connection achieve higher accuracy values of 99.71%, 98.66%, 97.59%, and 99.24% for UCM, AID, NWPU, and EuroSAT datasets, respectively, as compared to VGG19 and optimal guidance whale optimization algorithm–bidirectional long short-term memory.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"4188-4198"},"PeriodicalIF":4.7,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10815620","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106047","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":"Global Elevation Inversion for Multiband Spaceborne Lidar: Predevelopment of Forest Canopy Height","authors":"Haowei Zhang;Wei Gong;Hu He;Yue Ma;Weibiao Chen;Jiqiao Liu;Ge Han;Zhiyu Gao;Wanqi Zhong;Xin Ma","doi":"10.1109/JSTARS.2024.3522330","DOIUrl":"https://doi.org/10.1109/JSTARS.2024.3522330","url":null,"abstract":"Compared to single-band spaceborne lidars such as the global ecosystem dynamics investigation (GEDI) and Ice, Cloud and Land Elevation Satellite-2 (ICESat-2), multiband spaceborne lidars improve the detection of the canopy and ground. However, research on geographic elevation inversion with multi-band spaceborne lidars is limited, especially in developing algorithms that fully utilize multiple wavelengths for accurate measurements. A high-precision multiband fusion algorithm (MBFA) is proposed for global geographic elevation inversion for multiband spaceborne lidar of China's Daqi-1 satellite (DQ-1), enhancing the ranging capability of the 1572 nm channel by approximately 5 times. Compared with ICESat-2, GEDI and airborne scanning data in Finland, the geographic elevation results of MBFA showed average biases of –0.09, 0.1, and –0.95 m, with root mean square errors (RMSE) of 3.68, 4.51, and 7.70 m, respectively. Accurate forest canopy heights can be obtained using the decomposed signal approach in MBFA, which has been verified in Finland. The validation results (<italic>R</i>² = 0.72, RMSE = 1.38 m, BIAS = –0.75 m) demonstrate the DQ-1 satellite's effectiveness in measuring canopy height. The results indicate that the MBFA model has potential for global forest canopy height extraction and carbon sink research. The proposed MBFA can also provide guide for high-precision inversion of future multiband lidar satellites.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"2928-2941"},"PeriodicalIF":4.7,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10815618","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142993458","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":"Short-Range Nonstationary Clutter Suppression for Airborne KA-STAP Radar in Complex Terrain Environment","authors":"Yuanyi Xiong;Wenchong Xie;Yongliang Wang;Wei Chen;Ming Hou","doi":"10.1109/JSTARS.2024.3522257","DOIUrl":"https://doi.org/10.1109/JSTARS.2024.3522257","url":null,"abstract":"Due to the range ambiguity effect and the complex terrain environment, the remote weak target of interest for nonsidelooking airborne radar is usually superimposed with the nonstationary and heterogeneous short-range strong clutter, so it is difficult for the traditional space–time adaptive processing (STAP) methods to achieve effective moving target detection. Therefore, the detection of the remote weak moving target in the heterogeneous and nonstationary clutter environment is one of the difficulties encountered by airborne radar. Through the analysis of airborne radar clutter characteristics, we have found that the short-range nonstationary clutter and the long-range ambiguous clutter do not overlap in Doppler domain, so the homogeneous training samples can be effectively selected through the digital terrain database. On this basis, the article establishes the refined grid dot level clutter signal model of airborne radar and proposes a three-dimensional STAP (3D-STAP) method based on the digital terrain database, namely the DTD-3D-STAP method. The method first accurately registers the airborne radar echo with the grid dots of the digital terrain database; next, the training samples are selected along the equal Doppler line based on prior knowledge; then, the training samples can be compensated through power compensation; and finally, the short-range nonstationary clutter is suppressed through 3D-STAP technology. On one hand, the proposed method ensures the homogeneity of the clutter spectrum and power of the training samples, and the sample can be detected through prior knowledge and power compensation. On the other hand, 3D-STAP technology is used to effectively suppress nonstationary clutter. Computer simulation and experimental results verify the effectiveness of the proposed method.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"2766-2776"},"PeriodicalIF":4.7,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10815628","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142975848","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}