IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society最新文献_第4页

DeepPSF: On-Orbit Point Spread Function Estimation of Space Camera With Deep Learning 基于深度学习的空间相机在轨点扩展函数估计

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-04-21 DOI: 10.1109/LGRS.2025.3562763

Bo Wang;Hongyu Chen;Ying Lu;Jiantao Peng

{"title":"DeepPSF: On-Orbit Point Spread Function Estimation of Space Camera With Deep Learning","authors":"Bo Wang;Hongyu Chen;Ying Lu;Jiantao Peng","doi":"10.1109/LGRS.2025.3562763","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3562763","url":null,"abstract":"Obtaining an on-orbit space camera’s point spread function (PSF) is challenging but necessary for remote sensing image (RSI) restoration. Current methods rely on regular ground targets, causing determining the PSF at any position on the camera sensor to involve significant human effort and be highly inefficient. To reduce the difficulty and improve the precision of estimating the PSFs of an on-orbit space camera, this letter proposes DeepPSF, a novel PSF prediction method based on deep learning and Fourier transformation. DeepPSF employs a dual-stream convolutional neural network (CNN) to extract multiscale features from blurred and reference images, introduces a channel-wise Wiener filtering block for PSF feature calculation in frequency domain, and reconstructs high-precision PSF through a CNN network. Experiments demonstrate: 1) on synthetic datasets, DeepPSF achieves PSF prediction with 58.2 dB PSNR (SSIM > 0.64), significantly outperforming Wiener filtering and phase-only image (POI)-based kernel estimation method; 2) when combined with the nonblind deblurring algorithm DWDN, it delivers 26.1 dB restoration PSNR, surpassing comparative methods; and 3) real RSI tests validate its adaptability to complex scenarios. This method provides an efficient solution for full field-of-view PSF modeling of on-orbit cameras.","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":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144072856","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

Boundary SAM: Improved Parcel Boundary Delineation Using SAM’s Image Embeddings and Detail Enhancement Filters 边界SAM：利用SAM的图像嵌入和细节增强滤波器改进的包裹边界划分

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-04-21 DOI: 10.1109/LGRS.2025.3563023

Bahaa Awad;Isin Erer

{"title":"Boundary SAM: Improved Parcel Boundary Delineation Using SAM’s Image Embeddings and Detail Enhancement Filters","authors":"Bahaa Awad;Isin Erer","doi":"10.1109/LGRS.2025.3563023","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3563023","url":null,"abstract":"Accurate agricultural parcel boundary delineation is essential in remote sensing applications, yet traditional supervised methods require extensively annotated datasets and often fail to generalize across diverse landscapes. The segment anything model (SAM), a foundational model for zero-shot segmentation, provides scalability but struggles with certain remote sensing challenges, particularly agricultural parcels. In this letter, we propose a novel approach to enhance SAM’s performance by leveraging its embeddings to extract meaningful features. Our method applies principal component analysis (PCA) for dimensionality reduction, high-frequency decomposition, and guided filtering to enhance input images, aligning them better with SAM’s strengths. By refining the input data through these steps, we improve SAM’s ability to delineate parcel boundaries effectively. Experimental results demonstrate consistent improvements across SAM backbone sizes and parameter settings, achieving higher accuracy in segmentation metrics such as under-segmentation (US) rate, over-segmentation (OS) rate, intersection over union (IoU), and false negative (FN) rate.","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":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144073099","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

GPR Bscan Imaging Enhancement Method for Rebar Occlusion 钢筋遮挡的GPR Bscan成像增强方法

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-04-18 DOI: 10.1109/LGRS.2025.3562426

Qiguo Xu;Tao Zhang;Zebang Pang;Wentai Lei

{"title":"GPR Bscan Imaging Enhancement Method for Rebar Occlusion","authors":"Qiguo Xu;Tao Zhang;Zebang Pang;Wentai Lei","doi":"10.1109/LGRS.2025.3562426","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3562426","url":null,"abstract":"When using ground-penetrating radar (GPR) to detect targets below shallow rebar mesh in reinforced concrete structures, the strong scattering characteristics of rebar mesh cause distortion and interference of target echoes and lead to imaging artifacts and degradation. This letter proposes a coarse-scale and fine-scale dual-branch imaging enhancement network (CFD-IENet) to achieve target imaging under rebar mesh in reinforced concrete by combining Bscan echo data enhancement with back projection (BP) imaging result enhancement. First, a residual U (Res-U) network suppresses complex background clutter in Bscan data to improve the signal-to-noise ratio. Then, a coarse-scale and fine-scale dual-branch network is constructed to enhance both Bscan and BP imaging. In the Bscan enhancement stage, strong and weak signals are trained separately, aiming for surface rebar echo interference in reconstructing weak target signals beneath the rebar mesh. In the BP imaging enhancement stage, artifacts and multipath ghosts are suppressed to enhance occluded target imaging. A bilinear fusion module (BFM) is designed to facilitate global feature interaction, promoting the fusion of Bscan and BP imaging features across scales, thereby improving reconstruction and enhancement accuracy. The experimental results on cracks occluded by rebar mesh demonstrate the method’s effectiveness, showing a 4.73-dB improvement in peak signal-to-noise ratio (PSNR) and a 0.16 improvement in structural similarity (SSIM) index compared to the RNMF + BP + Unet enhancement 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":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143896500","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

Conditional Brownian Bridge Diffusion Model for VHR SAR to Optical Image Translation VHR SAR到光学影像转换的条件布朗桥扩散模型

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-04-18 DOI: 10.1109/LGRS.2025.3562401

Seon-Hoon Kim;Daewon Chung

{"title":"Conditional Brownian Bridge Diffusion Model for VHR SAR to Optical Image Translation","authors":"Seon-Hoon Kim;Daewon Chung","doi":"10.1109/LGRS.2025.3562401","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3562401","url":null,"abstract":"Synthetic aperture radar (SAR) imaging technology provides the unique advantage of being able to collect data regardless of weather conditions and time. However, SAR images exhibit complex backscatter patterns and speckle noise, which necessitate expertise for interpretation. Research on translating SAR images into optical-like representations has been conducted to aid the interpretation of SAR data. Nevertheless, existing studies have predominantly utilized low-resolution satellite imagery datasets and have largely been based on generative adversarial network (GAN) which are known for their training instability and low fidelity. To overcome these limitations of low-resolution data usage and GAN-based approaches, this letter introduces a conditional image-to-image translation approach based on Brownian bridge diffusion model (BBDM). We conducted comprehensive experiments on the MSAW dataset, a paired SAR and optical images collection of 0.5 m very-high-resolution (VHR). The experimental results indicate that our method surpasses both the conditional diffusion models (CDMs) and the GAN-based models in diverse perceptual quality metrics.","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":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143949223","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

Fine-Tuning SAM for Forward-Looking Sonar With Collaborative Prompts and Embedding 具有协同提示和嵌入的前视声纳微调SAM

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-04-18 DOI: 10.1109/LGRS.2025.3562182

Jiayuan Li;Zhen Wang;Nan Xu;Zhuhong You

{"title":"Fine-Tuning SAM for Forward-Looking Sonar With Collaborative Prompts and Embedding","authors":"Jiayuan Li;Zhen Wang;Nan Xu;Zhuhong You","doi":"10.1109/LGRS.2025.3562182","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3562182","url":null,"abstract":"The segment anything model (SAM) represents a significant advancement in semantic segmentation, particularly for natural images, but encounters notable limitations when applied to forward-looking sonar (FLS) images. The primary challenges lie in the inherent boundary ambiguity of FLS images, which complicates the use of prompt strategies for accurate boundary delineation, and the lack of effective interaction between prompts and image features. In this letter, we introduce a collaborative prompting (CP) strategy to address these issues by generating dense prompt embeddings and sonar tokens that focus on contour and boundary features, thereby replacing the original dense prompt embedding and intersection over union (IoU) token. To further enhance segmentation, we use embedding compensation techniques based on Mamba and Kolmogorov–Arnold network (KAN), which increase boundary information to image embeddings and improve the fusion of prompts within image embeddings. We conducted comprehensive experiments, including comparative analyses and ablation studies, to validate the superiority of our proposed approach. Results show that our method significantly improves segmentation performance for FLS images, effectively addressing boundary ambiguity and optimizing prompt utilization. The source code and dataset will be available on <uri>https://github.com/darkseid-arch/FLSSAM</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":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143896232","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

Spatial–Stratigraphic Information and Dynamic Range Attention Assist Well-Logging Lithological Interpretation 空间地层信息和动态范围的关注有助于测井岩性解释

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-04-18 DOI: 10.1109/LGRS.2025.3562350

Keran Li;Jinmin Song;Shugen Liu;Zhiwu Li;Di Yang;Wei Chen;Xin Jin;Chunqiao Yan;Shan Ren

{"title":"Spatial–Stratigraphic Information and Dynamic Range Attention Assist Well-Logging Lithological Interpretation","authors":"Keran Li;Jinmin Song;Shugen Liu;Zhiwu Li;Di Yang;Wei Chen;Xin Jin;Chunqiao Yan;Shan Ren","doi":"10.1109/LGRS.2025.3562350","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3562350","url":null,"abstract":"Time-series models, particularly CNN-bidirectional long short-term memory (BiLSTM) architectures, have shown advances in the lithological interpretation of well-logging data. However, CNN and attention mechanisms face challenges in training efficiency and predicting precision. To improve this deficiency, a dynamic and lightweight attention mechanism and a strategy that combines geological/spatial information have been proposed. This study introduces two novel enhancements: the spatial and stratigraphic information processing (shortened as Spatial and Strat) method and the dynamic range attention (DRA) mechanism. Spatial-stratigraphic context (SSP) integrates geological context by encoding depositional sequences as time series. DRA is a lightweight attention module that adaptively adjusts local attention ranges based on global context. Experiments on a collected dataset from the eastern Sichuan Basin (13 wells and 14 587 labeled samples) demonstrate that the proposed DRA-BiLSTM model with SSP achieves excellent performance, achieving accuracies of 0.99 on the training set, 0.97 on the validation set, and 0.92 on the testing set, with low error rates of 0.08 for Top-5 and 0.02 for Top-1. Ablation studies confirm the critical roles of SSP in capturing geological patterns and DRA in balancing computational efficiency by paying more attention to the vertical sedimentary process. These innovations significantly advance automated lithological interpretation, offering a robust framework for geophysical applications.","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":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144072852","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

CDxLSTM: Boosting Remote Sensing Change Detection With Extended Long Short-Term Memory CDxLSTM：扩展长短期记忆的遥感变化检测

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-04-18 DOI: 10.1109/LGRS.2025.3562480

Zhenkai Wu;Xiaowen Ma;Rongrong Lian;Kai Zheng;Wei Zhang

{"title":"CDxLSTM: Boosting Remote Sensing Change Detection With Extended Long Short-Term Memory","authors":"Zhenkai Wu;Xiaowen Ma;Rongrong Lian;Kai Zheng;Wei Zhang","doi":"10.1109/LGRS.2025.3562480","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3562480","url":null,"abstract":"In complex scenes and varied conditions, effectively integrating spatial-temporal context is crucial for accurately identifying changes. However, current remote sensing change detection (RS-CD) methods lack a balanced consideration of performance and efficiency. CNNs lack global context, transformers are computationally expensive, and Mambas face compute unified device architecture (CUDA) dependence and local correlation loss. In this letter, we propose CDxLSTM, with a core component that is a powerful xLSTM-based feature enhancer (FE) layer, integrating the advantages of linear computational complexity, global context perception, and strong interpretability. Specifically, we introduce a scale-specific FE layer, incorporating a cross-temporal global perceptron (CTGP) customized for semantic-accurate deep features, and a cross-temporal spatial refiner (CTSR) customized for detail-rich shallow features. In addition, we propose a cross-scale interactive fusion (CSIF) module to progressively interact global change representations with spatial responses. Extensive experimental results demonstrate that CDxLSTM achieves state-of-the-art performance across three benchmark datasets, offering a compelling balance between efficiency and accuracy. Code is available at <uri>https://github.com/xwmaxwma/rschange</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":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143918796","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

Optimizing Relative Radiometric Normalization: Minimizing Residual Distortions in Multispectral Bitemporal Images Using Trust-Region Reflective and Laplacian Pyramid Fusion 优化相对辐射归一化：利用信任区域反射和拉普拉斯金字塔融合最小化多光谱双时间图像的残留畸变

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-04-18 DOI: 10.1109/LGRS.2025.3562276

Armin Moghimi;Turgay Celik;Ali Mohammadzadeh;Saied Pirasteh;Jonathan Li

{"title":"Optimizing Relative Radiometric Normalization: Minimizing Residual Distortions in Multispectral Bitemporal Images Using Trust-Region Reflective and Laplacian Pyramid Fusion","authors":"Armin Moghimi;Turgay Celik;Ali Mohammadzadeh;Saied Pirasteh;Jonathan Li","doi":"10.1109/LGRS.2025.3562276","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3562276","url":null,"abstract":"Accurate relative radiometric normalization (RRN) is important for reliable multitemporal remote sensing image analysis. Traditional methods often depend on coregistered image pairs, limiting their applicability with unregistered data. Keypoint-based RRN (KRRN) relaxes this constraint but remains affected by residual radiometric errors due to normalization inaccuracies and nonlinear effects. This letter introduces a refinement strategy that leverages the trust-region reflective (TRR) algorithm to optimize normalization parameters, coupled with Laplacian pyramid (LP) fusion for seamless image integration. Evaluation on four multispectral image pairs from different sensors (e.g., Landsat 8 and Sentinel-2, IRS and Landsat 5, Landsat 7 and SPOT-5, and UK-DMC2 and Landsat 5) and one pair from the same sensor (Sentinel-2) showed that our method reduces residual radiometric discrepancies, achieving up to 29% lower RMSE than some well-known models. The source code and datasets are available on GitHub: <uri>https://github.com/ArminMoghimi/Tensor-based-keypoint-detection</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":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143896434","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

A Novel Truncated Capped Norm Regularization Method for Hyperspectral Image Denoising 一种新的截断帽范数正则化方法用于高光谱图像去噪

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-04-18 DOI: 10.1109/LGRS.2025.3562203

Xuegang Luo;Junrui Lv

引用次数: 0

Robust DOA Estimation Using Complex-Valued Residual Attention Networks With Low-Rank and Sparse Prior 基于低秩稀疏先验复值残差注意网络的鲁棒DOA估计

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-04-17 DOI: 10.1109/LGRS.2025.3562069

Zeqi Yang;Shuai Ma;Yiheng Liu;Hua Zhang;Xiaode Lyu

引用次数: 0