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

{"title":"Micro-Doppler Frequency Extraction and Scatterer Classification for a Smooth-Surfaced Cone-Shaped Precession Target Under Narrowband Radar","authors":"Dan Xu;Siyuan Zhao;Kaiming Li;Jiang Qian;Mengdao Xing;Maria Sabrina Greco;Fulvio Gini","doi":"10.1109/JSTARS.2025.3555068","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3555068","url":null,"abstract":"The micro-Doppler (μD) characteristics of ballistic targets are crucial for estimating motion and structural parameters, as well as for target recognition. However, existing time-frequency (TF) analysis methods are predominantly nonparametric and suffer from limited resolution, making it challenging to accurately extract μD TF curves. This limitation hampers further applications in this domain. Therefore, under narrowband radar observation conditions, this article proposes a method for μD TF characteristics extraction and scatterer type identification, specifically for smooth-surfaced cone-shaped precession targets. The method first utilizes sliding-window Root-MUSIC to extract the instantaneous μD frequencies of the target. Then, the inverse Radon transform (IRT) and You Only Look Once version 5 algorithm are applied to locate and identify the peaks of cone vertex and cone base after IRT. Based on the peaks, the μD TF trend curves can be reconstructed using the Radon transform (RT). The extracted instantaneous μD frequencies are then associated according to the trend curves, enabling the reconstruction of the μD frequencies for the cone vertex and cone base. Experiments validate the effectiveness and noise robustness of the proposed method. The results demonstrate that the estimation accuracy of the proposed method is independent of the sampling interval and significantly outperforms traditional nonparametric methods.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"9368-9379"},"PeriodicalIF":4.7,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10942342","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143835438","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":"Jointing Adjacent Environmental Variation Into a Deep Learning Model for Gap-Filling Passive Microwave-Based Land Surface Temperature","authors":"Weizhen Ji;Yunhao Chen;Haiping Xia;Han Gao;Lei Zhu","doi":"10.1109/JSTARS.2025.3554810","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3554810","url":null,"abstract":"Passive microwave-based land surface temperature (PMW LST) serves as a significant source for complementary thermal infrared LST, whereas the orbit gaps frequently result in missing data. Up to now, many studies have proposed methods to fill these gaps in PMW LST. However, most of these methods depend on the assumption that the missing LST is similar to that of adjacent days, yet the natural environment changes may lead to this assumption not being established. To address this, we proposed a comprehensive deep-learning model that incorporates three groups of natural variables, including atmosphere, land environment, and radiation, from both the target and adjacent days. Simultaneously, we employ two advanced microwave scanning radiometer (AMSR) LST-based simulated validations and six in-situ measurements to evaluate the model's gap-filling performance. According to the results, the proposed model achieves root mean squared error (RMSE) of 1.87 K/1.89 K and 1.69 K/1.71 K for the two AMSR LST-based validations during the daytime/nighttime. Compared with the inverse distance weighted method and an advanced deep learning model, the proposed approach improves 0.27–0.5 K (12.6% –22.6%) and 0.14–0.3 K (6.9% –14.9%) during daytime and nighttime, respectively. Furthermore, based on the results of six in-situ measurements, the gap-filled results gain the average RMSE of 3.7 K and 3.21 K during the daytime and nighttime, respectively. In addition, we find that the land environment and radiation conditions have a stronger impact during the daytime, while atmospheric conditions are more sensitive at night. These findings present a more scientific and effective gap-filling method, potentially enhancing the accuracy of land thermal environment research.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"9682-9700"},"PeriodicalIF":4.7,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10938895","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143835366","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":"Extracting Dwellings in Refugee Camps Using Multifractal Analysis and Mathematical Morphology Based Descriptors","authors":"Małgorzata Jenerowicz-Sanikowska;Anna Wawrzaszek","doi":"10.1109/JSTARS.2025.3546403","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3546403","url":null,"abstract":"This article presents an automatic procedure for detecting and counting dwellings in refugee/internally displaced persons camps. Very high resolution (VHR) satellite images are used to monitor camps, especially in inaccessible to “in-situ” measures areas. We develop a new algorithm to analyze these images, with the aim of improving detection accuracy and computing performance. The algorithm is based on local multifractal analysis and mathematical morphology, two methods that are becoming increasingly popular in the image analysis community. Our procedure translates the visual characterization of the desired structures into a morphological image processing chain. However, morphological filtering is not performed on the original image <italic>per se</i>, but on the image expressed by the Hölder exponent. Proposed method is applied to a set of VHR satellite images (GeoEye-1, WorldView-2, -3, -4 and JL-1GF02A) of two camps in Africa. Our technique is compared with results obtained by visual interpretation. The correlation coefficient between the two methods is 0.98, with an omission error of 7.98% and a commission error of 4.54%.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"8001-8010"},"PeriodicalIF":4.7,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10938975","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143706712","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 Urban Heat Island Analysis Through Multisensor Data Fusion and GRU-Based Deep Learning Approaches for Climate Modeling","authors":"Ning Tang;Muhammad Farhan;Pir Mohammad;M. Abdullah-Al-Wadud;Saddam Hussain;Umair Hamza;Rana Muhammad Zulqarnain;Nazih Yacer Rebouh","doi":"10.1109/JSTARS.2025.3554529","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3554529","url":null,"abstract":"Rapid urbanization and land-use changes have exacerbated the urban heat island (UHI) effect, threatening urban sustainability and climate resilience. This study uses a novel gated recurrent unit (GRU)-based deep learning model in addition to the Mann–Kendall trend, Pearson correlation, and continuous wavelet to investigate the UHI phenomenon in Multan city of Pakistan. The approach utilizes the normalized difference vegetation index (NDVI) and normalized difference built-up index (NDBI) as essential variables to forecast UHI accurately using a GRU-based deep learning model using a monthly Landsat dataset from 2001 to 2023. The results from the Mann–Kendall test indicated a minor increase in monthly UHI values, accompanied by notable seasonal fluctuations with a substantial decrease in winter (Tau = −3.486), whereas a notable increase is observed in the summer season (Tau = 0.158). The NDVI exhibited a notable annual increase (Tau = 3.43), suggesting enhanced vegetation health. Conversely, NDBI showed a significant decrease (Tau = −0.907). The result of Pearson's correlation study showed that UHI is significantly negatively correlated with NDVI and positively with NDBI, with a correlation coefficient of −0.540 and 0.344, respectively. Wavelet coherence analysis revealed considerable seasonal and annual relationships between UHI, NDVI, and NDBI. The GRU-based model achieved a coefficient of determination (R²) of 0.90 with an RMSE value of 0.09, indicating robust predictive performance. The SHAP (SHapley Additive explanations) analysis revealed that NDVI is the predictor with the most significant influence. The adopted approach emphasizes vegetation's crucial function in reducing UHI's effects and offers valuable insights for urban planning and measures to mitigate climate change.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"9279-9296"},"PeriodicalIF":4.7,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10938603","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143817987","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":"Eagle-YOLOv8: UAV Object Detection Inspired by the Eagle-Eye Vision System","authors":"Dianwei Wang;Zehao Gao;Jie Fang;Yuanqing Li;Zhijie Xu","doi":"10.1109/JSTARS.2025.3554821","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3554821","url":null,"abstract":"Object detection in unpiloted aerial vehicle (UAV) imagery has been widely applied across various domains. However, the typically small size and uneven spatial distribution of objects in UAV imagery pose significant challenges for UAV object detection tasks. Confronting such challenges, we propose Eagle-YOLOv8, an object detection algorithm for UAV imagery inspired by the eagle-eye vision system. First, inspired by the double fovea mechanism of the eagle eye, we construct a long-focus attention module, which can promote the network to focus on the target and pay more attention to discriminative features. Second, we propose a feature weight fusion network inspired by the double field of view characteristics of eagle eye. This network utilizes a novel weight fusion technique to alternative the conventional concatenate method, which assigns weights to feature layers according to their importance. Finally, we analyze the effect of wise-IoU loss on the fit of the prediction box to the object. In addition, we create a dataset called AerialDet with eight categories to validate the generalization performance of the proposed method. Experimental evaluations conducted on both the challenging VisDrone2019-Det dataset and our self-collected dataset validate the effectiveness of Eagle-YOLOv8. The proposed method outperforms baseline approaches in object detection performance, exhibiting notable improvements: 8.56% in precision metrics, 10.06% in mAP50 metrics, and 9.43% in recall metrics, all achieved with only a marginal increase in parameters compared to YOLOv8 (small).","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"9432-9447"},"PeriodicalIF":4.7,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10938892","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143835354","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 Machine-Learning-Based Ocean-Current Velocity Inversion Model Using OCN From Sentinel-1 Observations","authors":"Yang Bai;Yubin Zhang;Xudong Zhang;Xiaofeng Li","doi":"10.1109/JSTARS.2025.3554229","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3554229","url":null,"abstract":"High-precision ocean-current velocity inversion is crucial for maritime activities. Synthetic aperture radar (SAR) has become a key data source for ocean-current velocity inversion. However, traditional methods, such as the Doppler centroid anomaly (DCA) and along-track interferometry methods, face challenges, such as low inversion accuracy, poor robustness, and limited data sources. This study developed OCN-CIM, a machine-learning-based model that directly derives the radial ocean-current velocity from Sentinel-1 observations. The model is trained using 186 scenes of Sentinel-1 Level 2 ocean data (OCN) collected between 14 July 2020 and 16 May 2024, in regions with strong currents along the East Coast of the United States. The ground truth is obtained from matched high-frequency radar data. Built on a fully connected neural network, the OCN-CIM features a custom loss function focused on high ocean-current velocities. The model achieved a mean absolute error (MAE) of 0.16 m/s, root-mean-square error (RMSE) of 0.20 m/s, and mean deviation (MD) of 0.005 m/s on the test dataset. When applying the OCN-CIM to ten independent cases, the average MAE, RMSE, and MD were 0.13 m/s, 0.16 m/s, and −0.03 m/s, compared with 0.26 m/s, 0.34 m/s, and 0.06 m/s for the traditional DCA method, demonstrating significant improvement in inversion accuracy. In addition, the OCN-CIM exhibits robustness, with reduced sensitivity to local wind and SAR data anomalies, and consistent results across various electromagnetic direction error-correction methods.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"9622-9635"},"PeriodicalIF":4.7,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10938213","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143835469","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":"Freshness-Aware Device-to-Device Communication in Digital Twin Network for Disaster Management","authors":"Saurabh Chandra;Prateek;Rajeev Arya;Rohit Sharma;Kusum Yadav;Michał Gandor","doi":"10.1109/JSTARS.2025.3554302","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3554302","url":null,"abstract":"In a dynamic social Internet of Things (SIoT) environment, maintaining information freshness is vital for effective disaster management. This article models a digital twin-assisted SIoT network leveraging device-to-device (D2D) communication to address the critical challenge of minimizing the age of information (AoI). A digital twin-enabled AoI-driven framework is proposed to quantify freshness and ensure real-time freshness updates of devices in disaster scenarios. The problem is formulated to optimize AoI under imperfect channel state information (CSI), by incorporating signal strength, latency, and resource allocation constraints. The <underline>p</u>roposed <underline>d</u>igital <underline>t</u>win <underline>s</u>patio-temporal <underline>a</u>pproach (PDTSA) utilizes a spatio-temporal and hyperplane transformation concept for efficient resource management. By enabling D2D nodes to autonomously adapt transmission power and channel gain realization, the framework effectively minimizes AoI even in scenarios with limited CSI. Simulation results validate the performance of the proposed PDTSA achieving a 55.59% reduction in AoI and around a 12.1% increase in throughput compared to benchmark schemes. These results demonstrate the potential of the approach to enhance network efficiency and reliability, making it a promising solution for disaster management systems powered by SIoT. The proposed method may lead to an efficient solution for disaster management systems driven by the SIoT.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"9076-9083"},"PeriodicalIF":4.7,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10938411","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143830569","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":"PMGMCN: A Parallel Dynamic Multihop Graph and Composite Multiscale Convolution Network for Hyperspectral Sparse Unmixing","authors":"Kewen Qu;Huiyang Wang;Mingming Ding;Xiaojuan Luo;Fangzhou Luo","doi":"10.1109/JSTARS.2025.3549515","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3549515","url":null,"abstract":"In recent years, sparse unmixing (SU) has garnered significant attention in hyperspectral images (HSI) because it does not require endmember estimation, relying instead on prior spectral libraries to represent observed HSI data, which avoids the influence of endmember extraction on unmixing. However, SU methods based on representation models have limited capability in learning nonlinear features, which results in poor abundances estimation performance in complex environments. Recently, inspired by deep learning, SU models based on neural networks have been proposed to more effectively extract and handle nonlinear features. Nevertheless, the convolution strategies employed in existing SU network models lead to insufficient attention to long-range pixel dependencies, consequently resulting in restricted utilization of spatial priors. In view of the abovementioned shortcomings, this article proposes a parallel dynamic multihop graph and composite multiscale convolution network for SU, referred to as PMGMCN. The network combines the advantages of convolutional neural network (CNN) and graph convolutional network (GCN), achieving a complementary and enhanced integration of their characteristics. Specifically, the network captures long-range spatial features through the designed dynamic multihop graph interaction attention module, which is based on GCN, while the composite multiscale convolution spatial–spectral attention module, which is based on CNN, is designed to extract multiscale spatial–spectral information within local regions. In addition, this article introduces an adaptive weighted total variation loss function based on Sobel edge operator and Gaussian function to encourage piecewise smoothness in abundances maps while preserving edge information. Extensive experiments on synthetic and real datasets demonstrate the effectiveness and superiority of the proposed method compared with the state-of-the-art methods.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"8438-8456"},"PeriodicalIF":4.7,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10937499","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143726545","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":"Saturated Interference in SAR: Theoretical Analysis and Suppression Solutions","authors":"Lunhao Duan;Xingyu Lu;Jianchao Yang;Huizhang Yang;Shiyuan Zhang;Guanqi Tong;Ke Tan;Zheng Dai;Hong Gu","doi":"10.1109/JSTARS.2025.3554199","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3554199","url":null,"abstract":"Synthetic aperture radar (SAR) systems are highly sensitive to interference, making interference suppression a critical concern. While significant research has been conducted on SAR interference suppression using parametric, nonparametric, semiparametric methods, and machine learning, these studies often neglect the impact of high-power interference, which can saturate SAR receivers due to their limited dynamic range. This saturation can lead to the model mismatch and performance degradation of many previously studied suppression methods. In this article, we provide a detailed analysis of saturated interference in SAR systems. We develop time-domain saturation signal models for different types of interference, and analyze the frequency domain spurious characteristics of saturated interference. Based on this analysis, we propose novel interference suppression method adapted for saturated narrowband and wideband interference. We validate our methods using semiactual data from Radarsat-1, demonstrating their effectiveness in suppressing both narrowband and wideband saturated interference. Besides, we also conducted analysis and verification using the real saturated interference data of Gaofen-3. The results confirm the proposed methods' capability to enhance SAR performance under saturation conditions.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"9244-9261"},"PeriodicalIF":4.7,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10938179","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143818031","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":"Multitask Change-Aware Network and Semisupervised Enhanced Multistep Training for Semantic Change Detection","authors":"Yifei Si;Jie Jiang","doi":"10.1109/JSTARS.2025.3554272","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3554272","url":null,"abstract":"Semantic change detection (SCD) aims to find out where and what changes between a pair of co-registered remote sensing images. Compared to binary change detection, which only predicts the location of changes, SCD provides detailed from-to change information, helping to gain a comprehensive understanding and analysis of land cover and land use. SCD is a challenging task due to the complexity of scenes in remote sensing images and the lack of semantic labels in SCD datasets. In this work, we propose a model named Multitask Change-Aware Network (MTCAN) and a Multistep Training (MST) method for land cover semantic change detection in optical remote sensing images. To better identify fine-grained semantic changes, the MTCAN comprises feature aggregation module (FAM), spatial enhancement module (SEM), and change extraction module (CEM). FAM integrates low-level spatial details and high-level semantics from multilevel features, which helps to capture small-sized changes. SEM models long-range correlations and global context, providing global representations in binary change detection and semantic segmentation branches. CEM extracts discriminative change features by calibrating differential features with channel and spatial attention, which helps to accurately locate change areas. MST is designed to overcome the insufficient training caused by the lack of semantic labels, consisting of contrastive loss and iterative self-training. The contrastive loss supervises the semantic segmentation parts with binary change labels. In the self-training process, the trained student model is added to the teacher model ensemble that generates pseudo labels for unlabeled areas, which are then used to train the next student. MTCAN-MST achieves 23.48% SeK on SECOND dataset and 67.74% SeK on Landsat-SCD dataset, outperforming the state-of-the-art methods with lower computational cost.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"9605-9621"},"PeriodicalIF":4.7,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10938184","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143835468","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}