IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society最新文献_第2页

A Novel Residue Degenerate Phase Unwrapping Method Using the L¹-Norm

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-03-10 DOI: 10.1109/LGRS.2025.3549511

YanDong Gao;Chao Yan;Wei Zhou;NanShan Zheng;YaChun Mao;ShiJin Li;BinHe Ji;Hefang Bian

{"title":"A Novel Residue Degenerate Phase Unwrapping Method Using the L¹-Norm","authors":"YanDong Gao;Chao Yan;Wei Zhou;NanShan Zheng;YaChun Mao;ShiJin Li;BinHe Ji;Hefang Bian","doi":"10.1109/LGRS.2025.3549511","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3549511","url":null,"abstract":"As we all know, phase unwrapping (PhU) is one of the key steps affecting interferometric synthetic aperture radar (InSAR) data processing. However, due to the residues, it is difficult to obtain ideal results in the areas with high noise and large-gradient changes. Therefore, how to effectively deal with residues becomes the top priority of the PhU. To address this issue, in this letter, a novel residue degenerate PhU (RDPhU) method is proposed. We use the fast iterative shrinkage thresholding algorithm (FISTA) to solve the residue degradation problem, which introduces a novel branch-cut strategy that can effectively prevent error propagation. To the best of our knowledge, FISTA is first applied to the PhU residues degradation problem. In addition, we introduce regularization theory into <inline-formula> <tex-math>$L^{1}$ </tex-math></inline-formula>-norm PhU to further improve the robustness of PhU. More interestingly, the RDPhU method can effectively solve the problem of low accuracy of PhU in the areas with large-gradient changes, while the PhU efficiency of the RDPhU method is greatly improved. Through simulation and TanDEM-X InSAR datasets, it is proved that the proposed method is an efficient and high-accuracy PhU method.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"22 ","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143698345","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Syn2Real Domain Generalization for Underwater Mine-Like Object Detection Using Side-Scan Sonar

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-03-10 DOI: 10.1109/LGRS.2025.3550037

Aayush Agrawal;Aniruddh Sikdar;Rajini Makam;Suresh Sundaram;Suresh Kumar Besai;Mahesh Gopi

{"title":"Syn2Real Domain Generalization for Underwater Mine-Like Object Detection Using Side-Scan Sonar","authors":"Aayush Agrawal;Aniruddh Sikdar;Rajini Makam;Suresh Sundaram;Suresh Kumar Besai;Mahesh Gopi","doi":"10.1109/LGRS.2025.3550037","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3550037","url":null,"abstract":"Underwater mine-like object (MLO) detection with deep learning suffers from limitations due to the scarcity of real-world side-scan sonar (SSS) data. This scarcity leads to overfitting, where models perform well on training data but poorly on unseen data. In this letter, we propose a synthetic to real (Syn2Real) domain generalization approach using diffusion models to address this challenge. Synthetic data generated by DDPM and DDIM models effectively enhances the training dataset. The residual noise in the final sampled images improves the model’s ability to generalize to real-world data with inherent noise and high variation. The baseline mask-region-based convolutional neural network (RCNN) model when trained on a combination of synthetic and original SSS training datasets, exhibited approximately a 35% increase in average precision (AP) compared to being trained solely on the original training data. This significant improvement highlights the potential of Syn2Real domain generalization for underwater mine detection.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"22 ","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143716480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

High-Precision Time Delay Calibration for Radio Astronomy Radars Based on Maximum Likelihood Iteration

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-03-10 DOI: 10.1109/LGRS.2025.3549788

Quanhua Liu;Bowen Cai;Xinliang Chen;Rui Zhu;Zhennan Liang

{"title":"High-Precision Time Delay Calibration for Radio Astronomy Radars Based on Maximum Likelihood Iteration","authors":"Quanhua Liu;Bowen Cai;Xinliang Chen;Rui Zhu;Zhennan Liang","doi":"10.1109/LGRS.2025.3549788","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3549788","url":null,"abstract":"In the calibration of distributed radar for radio astronomy, deep space radio sources are commonly used as calibration sources to correct interarray delay errors, and accurate delay estimation is critical. Traditional correlation methods are limited by sampling frequency, achieving accuracy only at the sampling interval level. To achieve higher accuracy, subsample estimation is necessary. This letter proposes a precise delay calibration method using maximum likelihood iteration for subsample delay estimation. The proposed algorithm starts with the frequency domain features, first transforming the delay estimation problem into a phase estimation problem, and then calculating the likelihood function of the phase difference. A cost function is established based on the maximum likelihood criterion, and the optimal solution is obtained using the Newton iteration method. Compared to other algorithms, the proposed algorithm achieves superior accuracy in subsample delay estimation, meeting stringent calibration requirements in radio astronomy. Simulation and experimental results verify the validation of the algorithm.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"22 ","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143706708","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Learnable Gabor Filters in Attention Unet for Prestack Seismic Inversion

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-03-07 DOI: 10.1109/LGRS.2025.3548983

Yizhen Shan;Yueming Ye;Bangyu Wu

{"title":"Learnable Gabor Filters in Attention Unet for Prestack Seismic Inversion","authors":"Yizhen Shan;Yueming Ye;Bangyu Wu","doi":"10.1109/LGRS.2025.3548983","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3548983","url":null,"abstract":"Amplitude variation with angle (AVA) prestack seismic inversion plays a critical role in oil and gas exploration and mineral resource assessment. Recently, deep learning methods, particularly convolutional neural networks (CNNs), have been widely adopted for seismic inversion. However, many of these methods, especially supervised learning, struggle with poor generalization and noise resistance. Seismic data contains rich texture information that can be used as prior to constrain the convolutional kernels of the network. Gabor functions have long been used for seismic data representation, and learnable Gabor filters improve upon this by dynamically extracting latent seismic data information via adaptively updating Gabor filter parameters. In this letter, we propose a multitask AVA inversion method using learnable Gabor filters within a 2-D multitask attention U-Net. We equip the network’s first layer with learnable Gabor filters for latent seismic data feature extraction to enhance both generalization and noise resistance. An adaptive weight update method (AWUM) is employed to balance multitask learning efficiency and generalization performance. By creating a training dataset that combines synthetic and field seismic data with corresponding labels, we integrate field samples into the network training. Experiments for both synthetic and field datasets demonstrate that the proposed method exhibits superior generalization and stability compared to several existing approaches.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"22 ","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143716497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep Spiking Neural Network for Energy-Efficient SAR Ship Detection

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-03-07 DOI: 10.1109/LGRS.2025.3549108

Minjung Yoo;Juhyeon Han;Sunok Kim

引用次数: 0

Unsupervised Diffusion Model for Seismic Deconvolution

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-03-07 DOI: 10.1109/LGRS.2025.3549055

Hongzhi Yu;Wenchao Chen;Xiaokai Wang;Dawei Liu

引用次数: 0

BKD-CL: Balanced Knowledge Distillation-Contrastive Learning for Distribution-Unknown Generalized Category Discovery in SAR ATR

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-03-07 DOI: 10.1109/LGRS.2025.3548978

Qianru Hou;Zhiwen Duan;Jianping Zong;Jianda Han;Hongpeng Wang

{"title":"BKD-CL: Balanced Knowledge Distillation-Contrastive Learning for Distribution-Unknown Generalized Category Discovery in SAR ATR","authors":"Qianru Hou;Zhiwen Duan;Jianping Zong;Jianda Han;Hongpeng Wang","doi":"10.1109/LGRS.2025.3548978","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3548978","url":null,"abstract":"Open-environment machine learning is crucial for category discovery in synthetic aperture radar automatic target recognition (SAR ATR). However, SAR ATR toward intelligent applications requires addressing not only open-world distributions but also data imbalance. In this letter, we first propose the distribution-unknown generalized category discovery (DUGCD) problem and introduce the balanced knowledge distillation-contrastive learning (BKD-CL) framework, which includes the frequency attention ViT (FAViT) module and a multilayer perceptron (MLP) projection head. Second, we optimize the loss function using both supervised and self-supervised contrastive learning methods to learn feature representations from labeled and unlabeled data. We also implement self-distillation and entropy regularization to facilitate knowledge training for a parameterized classifier aimed at classification learning. Finally, to tackle the issue of data imbalance, we introduce balanced knowledge distillation, which selectively transfers knowledge using weighted coefficients to address the poor recognition performance caused by imbalanced data distributions. Extensive experiments conducted on the MSTAR dataset demonstrate the superiority of our proposed method.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"22 ","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143667473","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Azimuth Multichannel SAR Signal Recovery for One Channel Data Completely Missing

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-03-07 DOI: 10.1109/LGRS.2025.3548989

Zonglin Yang;Zhimin Zhang;Huaitao Fan;Chen Zhen;Yongwei Zhang

引用次数: 0

Dual-Branch Cross Weighting Network for Multimodal Hyperspectral Unmixing

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-03-07 DOI: 10.1109/LGRS.2025.3549218

Ziang Jiao;Yongsheng Dong;Chongchong Mao;Lintao Zheng

{"title":"Dual-Branch Cross Weighting Network for Multimodal Hyperspectral Unmixing","authors":"Ziang Jiao;Yongsheng Dong;Chongchong Mao;Lintao Zheng","doi":"10.1109/LGRS.2025.3549218","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3549218","url":null,"abstract":"Hyperspectral unmixing has recently attracted much attention in the field of spectral image analysis. Unsupervised methods based on autoencoder can achieve excellent unmixing performance. However, these methods have unsatisfactory unmixing results for different substances with similar materials in complex scenarios. In this letter, we propose a new dual-branch cross weighting network (DCWNet) for multimodal hyperspectral unmixing. It can not only use the spectral feature information of spectral images but also acquire the spatial feature information of light detection and ranging (LiDAR) data simultaneously. Specifically, we build a spatial channel augmentation (SCA) block to help the network acquire spatial information more accurately from the horizontal, vertical, and channel aspects, respectively. We further construct an adaptive feature selection module for effectively using the spectral feature information and spatial feature information to better focus on the discrimination of materials in the scene through weighting fusion. Experimental results on two real multimodal datasets demonstrate the competitiveness and effectiveness of our proposed DCWNet in comparison to five representative methods.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"22 ","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143716498","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Robust Sparse Unmixing via Continuous Mixed Norm to Address Mixed Noise

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-03-06 DOI: 10.1109/LGRS.2025.3548697

Jincheng Gao;Jiayu Shi;Fei Zhu

{"title":"Robust Sparse Unmixing via Continuous Mixed Norm to Address Mixed Noise","authors":"Jincheng Gao;Jiayu Shi;Fei Zhu","doi":"10.1109/LGRS.2025.3548697","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3548697","url":null,"abstract":"Sparse unmixing, a critical task in hyperspectral image interpretation, aims to identify an optimal subset of endmembers from a predefined library and estimate the fractional abundances for each pixel. However, in real-world scenarios, various types of noise significantly degrade the performance of conventional sparse unmixing methods that usually rely on <inline-formula> <tex-math>$ell _{2}$ </tex-math></inline-formula>-norm loss function. To address this issue, this letter proposes a robust sparse unmixing method based on the continuous mixed norm (CMN), which exhibits resilience to mixed noise, particularly non-Gaussian impulsive noise. By adopting CMN as the reconstruction loss function, we formulate both the standard sparse unmixing problem and its augmented version with total variation (TV) regularizer for spatially piecewise smoothness. The corresponding algorithms are derived using the alternating direction method of multipliers (ADMM). Experiments on both synthetic and real hyperspectral datasets validate the effectiveness and robustness of the proposed method in handling diverse and mixed noise conditions over comparing methods. The code is available at: <uri>https://github.com/JinchengGao/CMNSU</uri>.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"22 ","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143688123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0