IEEE Transactions on Geoscience and Remote Sensing最新文献_第8页

Ultra-Wideband Mutual RFI Mitigation Between SAR Satellites: From the Perspective of European Sentinel-1A 从欧洲哨兵-1A 的角度看合成孔径雷达卫星之间的超宽带相互射频干扰缓解：欧洲哨兵-1A 的视角

IF 8.2 1区地球科学

IEEE Transactions on Geoscience and Remote Sensing Pub Date : 2024-11-18 DOI: 10.1109/tgrs.2024.3501309

Ning Li, Xingwang Hu

引用次数: 0

Arbitrary Dipole-Dipole Observation Systems and High-Precision Resistivity Imaging Algorithms for Complex Survey Areas 适用于复杂勘测区域的任意偶极-偶极观测系统和高精度电阻率成像算法

IF 7.5 1区地球科学

IEEE Transactions on Geoscience and Remote Sensing Pub Date : 2024-11-18 DOI: 10.1109/TGRS.2024.3499978

Peng Bai;Dongdong Zhao;Chao Li;Shengjie Qiao;Zhengyu Liu

{"title":"Arbitrary Dipole-Dipole Observation Systems and High-Precision Resistivity Imaging Algorithms for Complex Survey Areas","authors":"Peng Bai;Dongdong Zhao;Chao Li;Shengjie Qiao;Zhengyu Liu","doi":"10.1109/TGRS.2024.3499978","DOIUrl":"10.1109/TGRS.2024.3499978","url":null,"abstract":"Electrical resistivity tomography (ERT) plays a crucial role in resource development and hazard assessment in both urban and mountainous areas. However, conventional 3-D ERT, which relies on a regular grid layout, often faces limitations in acquiring sufficient and effective observational data in complex survey areas. Moreover, due to the influence of electric field volume effects, traditional gradient-based inversion algorithms struggle to achieve high-precision imaging results and interpretations. To address these challenges, we propose a new scheme that combines an arbitrary dipole-dipole acquisition system with a high-precision inversion imaging technique based on the supervised descent method (SDM). The arbitrary dipole-dipole acquisition system offers enhanced flexibility in electrode deployment, enabling the collection of a larger volume of observational data with richer polarization information, thereby laying a solid foundation for high-resolution exploration imaging. The high-precision SDM inversion technique integrates the powerful nonlinear fitting capabilities of neural networks with the physical laws governing electric field propagation, significantly improving the resolution of inversion imaging results. Numerical simulations confirm that, compared to conventional ERT, the arbitrary dipole-dipole acquisition system enables the gathering of more abundant and effective observational data in complex measurement environments. In addition, the simulation results demonstrate the superior performance of SDM over the Gauss-Newton (GN) method and the pure data-driven network inversion (PDNI) method in terms of imaging quality, computational efficiency, and generalization ability.","PeriodicalId":13213,"journal":{"name":"IEEE Transactions on Geoscience and Remote Sensing","volume":"62 ","pages":"1-12"},"PeriodicalIF":7.5,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142670509","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SAR Image Speckle Reduction Based on Nuclear Norm Minus Frobenius Norm Regularization 基于核规范减弗罗贝尼斯规范正则化的合成孔径雷达图像斑点减少技术

IF 8.2 1区地球科学

IEEE Transactions on Geoscience and Remote Sensing Pub Date : 2024-11-18 DOI: 10.1109/tgrs.2024.3501314

Fuyu Bo, Xiaole Ma, Yigang Cen, Shaohai Hu

引用次数: 0

Optimal Fusion Based Target Detection With Multichannel ATI SAR 利用多通道 ATI SAR 进行基于融合的最佳目标探测

IF 8.2 1区地球科学

IEEE Transactions on Geoscience and Remote Sensing Pub Date : 2024-11-18 DOI: 10.1109/tgrs.2024.3499960

Min Tian, Bin Liao

引用次数: 0

GPR Closed-Loop Denoising Based on Bandpass Filtering Constraints 基于带通滤波约束的 GPR 闭环去噪

IF 7.5 1区地球科学

IEEE Transactions on Geoscience and Remote Sensing Pub Date : 2024-11-18 DOI: 10.1109/TGRS.2024.3498868

Xianghao Liu;Sixin Liu;Zhuo Jia;Declan Vogt;Sen Tian;Xintong Liu;Qi Lu

{"title":"GPR Closed-Loop Denoising Based on Bandpass Filtering Constraints","authors":"Xianghao Liu;Sixin Liu;Zhuo Jia;Declan Vogt;Sen Tian;Xintong Liu;Qi Lu","doi":"10.1109/TGRS.2024.3498868","DOIUrl":"10.1109/TGRS.2024.3498868","url":null,"abstract":"Noise attenuation is crucial in ground-penetrating radar (GPR) data processing. In recent years, deep learning (DL) methods have shown excellent performance in GPR denoising tasks, but they typically focus only on recovering the target signal, which can lead to over-denoising. To enhance the generalizability and the practicality of denoising networks, we propose a strategy to generate random dielectric models from natural image datasets, which can quickly construct model datasets with low redundancy and reasonable distribution. To enhance the fidelity of GPR denoising, we leverage the powerful nonlinear fitting capabilities of convolutional neural networks (CNNs) and introduce a closed-loop denoising network framework for GPR. The framework consists of a denoising sub-network and a noise extraction sub-network, effectively achieving signal-noise separation in noised GPR data. Specifically, the denoising sub-network is used to recover weak reflection signals and initially remove noise, while the noise extraction sub-network is used to restore the true noise, mitigating the problem of over-denoising. A key innovation of our approach is the integration of bandpass filtering, which enhances the robustness of network training and supports effective weak signal recovery. This network framework forms a closed loop through the residual loss between the signal-noise separation results and the noised GPR data, the closed-loop structure is capable of further refining the signal and noise prediction results of the two subnetworks, thereby enhancing the numerical accuracy of the signal-to-noise separation results. Finally, the effectiveness of the GPR closed-loop denoising network is verified from multiple perspectives using both synthetic and field measured data. The results indicate that our proposed method is more competitive in GPR denoising tasks.","PeriodicalId":13213,"journal":{"name":"IEEE Transactions on Geoscience and Remote Sensing","volume":"62 ","pages":"1-14"},"PeriodicalIF":7.5,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142670595","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

High-Resolution Remote Sensing Image Change Detection Based on Fourier Feature Interaction and Multiscale Perception 基于傅立叶特征交互和多尺度感知的高分辨率遥感图像变化检测

IF 7.5 1区地球科学

IEEE Transactions on Geoscience and Remote Sensing Pub Date : 2024-11-18 DOI: 10.1109/TGRS.2024.3500073

Yongqi Chen;Shou Feng;Chunhui Zhao;Nan Su;Wei Li;Ran Tao;Jinchang Ren

{"title":"High-Resolution Remote Sensing Image Change Detection Based on Fourier Feature Interaction and Multiscale Perception","authors":"Yongqi Chen;Shou Feng;Chunhui Zhao;Nan Su;Wei Li;Ran Tao;Jinchang Ren","doi":"10.1109/TGRS.2024.3500073","DOIUrl":"10.1109/TGRS.2024.3500073","url":null,"abstract":"As a significant means of Earth observation, change detection in high-resolution remote sensing images has received extensive attention. Nevertheless, the variability in imaging conditions introduces style discrepancies and a range of pseudochange regions between bitemporal image pairs. Furthermore, changing objects possess diverse morphological representations, which makes accurately identifying change areas and delineating their boundaries within complex object distributions increasingly difficult. In response to the aforementioned challenges, we propose the Fourier feature interaction and multiscale perception (FIMP) model for effective change detection. To mitigate the impact of style discrepancies, FIMP employs the Fourier transform to adaptively filter bitemporal features in the frequency domain while mining the optimized bitemporal features relevant to the change detection task. To enhance the ability to recognize multiscale changing objects, FIMP aggregates and emphasizes the change areas with the introduced temporal change enhancement module (TCEM). By utilizing the U-fusion change perception module (UCPM) to perform multilevel bidirectional fusion of change features at different scales, FIMP can further enhance the ability to delineate complex semantic change boundaries. Experiments on three public datasets show that our approach outperforms seven state-of-the-art methods.","PeriodicalId":13213,"journal":{"name":"IEEE Transactions on Geoscience and Remote Sensing","volume":"62 ","pages":"1-15"},"PeriodicalIF":7.5,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142670599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A data-driven method for direct estimation of global 8-day 500 m ecosystem water use efficiency 直接估算全球 8 天 500 米生态系统用水效率的数据驱动法

IF 8.2 1区地球科学

IEEE Transactions on Geoscience and Remote Sensing Pub Date : 2024-11-18 DOI: 10.1109/tgrs.2024.3501411

Lingxiao Huang, Yifei Sun, Na Yao, Meng Liu

引用次数: 0

Transformer Tracking for Satellite Video: Matching, Propagation, and Prediction 卫星视频的变压器跟踪：匹配、传播和预测

IF 7.5 1区地球科学

IEEE Transactions on Geoscience and Remote Sensing Pub Date : 2024-11-18 DOI: 10.1109/TGRS.2024.3501380

Manqi Zhao;Shengyang Li;Jian Yang

{"title":"Transformer Tracking for Satellite Video: Matching, Propagation, and Prediction","authors":"Manqi Zhao;Shengyang Li;Jian Yang","doi":"10.1109/TGRS.2024.3501380","DOIUrl":"10.1109/TGRS.2024.3501380","url":null,"abstract":"Recently, transformer-based trackers have brought overwhelming advantages in general video. However, their performance in satellite video has been hindered by insufficient satellite-specific training and a lack of designs tailored to satellite targets and scene characteristics. To tackle these challenges, we propose a novel transformer-based tracking framework for satellite video object tracking: Transformer Matching, Propagation, and Prediction (TransMPP). TransMPP combines three stages: static matching, dynamic propagation, and prediction, to ensure accurate tracking in satellite videos. Specifically, the Matching model uses a one-stream pipeline for simultaneous feature extraction and relationship modeling across extensive search and template areas, thereby improving foreground and background discrimination capabilities. In addition, the Propagation and Prediction models enhance temporal modeling capabilities through local long-term and short-term feature propagation and global sequence prediction, respectively, boosting tracking robustness. Moreover, to ensure a fair comparison and evaluation, we also developed SatSOT-train, a large-scale training dataset for the SatSOT benchmark. After comprehensive training, TransMPP demonstrates state-of-the-art (SOTA) performance on the SatSOT dataset, achieving an area under the curve (AUC) score of 59.9% and a precision score of 71.5%, bringing improvements of 6.3% and 5.3%, respectively. The code will be available at \u0000<uri>https://github.com/DonDominic/TransMPP</uri>\u0000.","PeriodicalId":13213,"journal":{"name":"IEEE Transactions on Geoscience and Remote Sensing","volume":"62 ","pages":"1-16"},"PeriodicalIF":7.5,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142670605","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Multi-Branch Embedding Network With Bi-Classifier for Few-Shot Ship Classification of SAR Images 利用双分类器对合成孔径雷达图像中的少量舰船进行分类的多分支嵌入网络

IF 8.2 1区地球科学

IEEE Transactions on Geoscience and Remote Sensing Pub Date : 2024-11-18 DOI: 10.1109/tgrs.2024.3500034

Gui Gao, Meixiang Wang, Ping Zhou, Libo Yao, Xi Zhang, Hengchao Li, Gaosheng Li

引用次数: 0

ELunarDTMNet: Efficient reconstruction of high-resolution lunar DTM from single-view orbiter images ELunarDTMNet：从单视角轨道器图像高效重建高分辨率月球 DTM

IF 8.2 1区地球科学

IEEE Transactions on Geoscience and Remote Sensing Pub Date : 2024-11-18 DOI: 10.1109/tgrs.2024.3501153

Hao Chen, Philipp Gläser, Xuanyu Hu, Konrad Willner, Yongjie Zheng, Friedrich Damme, Lorenzo Bruzzone, Jürgen Oberst

引用次数: 0