IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society最新文献_第9页

Enhanced Multiscale Pyramid Deep Image Prior for Unsupervised Remote Sensing Image Restoration 基于增强多尺度金字塔深度图像先验的无监督遥感图像恢复

IF 4.4

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2026-01-01 Epub Date: 2026-09-22 DOI: 10.1109/LGRS.2026.3735466

D. Richard Samuel;N. S. Anandkumar;Jaya Saxena

{"title":"Enhanced Multiscale Pyramid Deep Image Prior for Unsupervised Remote Sensing Image Restoration","authors":"D. Richard Samuel;N. S. Anandkumar;Jaya Saxena","doi":"10.1109/LGRS.2026.3735466","DOIUrl":"https://doi.org/10.1109/LGRS.2026.3735466","url":null,"abstract":"The deep image prior (DIP) restores images without training data by exploiting the implicit bias of convolutional architectures, but suffers from slow convergence, limited texture fidelity, and inconsistent quality on satellite imagery. Enhanced multiscale pyramid DIP (EMSP-DIP) extends the DIP framework with: 1) coarse-to-fine hierarchical training with cross-scale consistency; 2) structured multifrequency input noise; and 3) a leak-safe compound loss combining gradient, edge, and boundary-transition terms, all evaluated on cloud-free pixels only. On RICE II images that span cloud coverage of 0.1%–87.2%, EMSP-DIP improves over baseline DIP by +5.46 dB PSNR and +0.043 SSIM (paired Wilcoxon <inline-formula> <tex-math>$plt 10^{-3}$ </tex-math></inline-formula>), in every coverage tier. When an exact cloud mask is available, classical biharmonic interpolation remains superior on pixel-level metrics; EMSP-DIP narrows this gap and overtakes it in the most heavily occluded scenes (36%–87% coverage), where boundary information becomes scarce. A complete <inline-formula> <tex-math>$2^{3}$ </tex-math></inline-formula>-component ablation is reported, together with a seed-paired comparison over 45 pairs and three seeds, which establishes that compound loss improves all four metrics (<inline-formula> <tex-math>$plt 0.05$ </tex-math></inline-formula> in each seed), a frequency-domain analysis of input noise, and a study of hyperparameter sensitivity.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"23 ","pages":"6020805-6020805"},"PeriodicalIF":4.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149131562","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

2.5-D Helicopter Transient Electromagnetic Data Inversion Method Based on SE-TransUNet 基于SE-TransUNet的2.5维直升机瞬变电磁数据反演方法

IF 4.4

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2026-01-01 Epub Date: 2026-03-19 DOI: 10.1109/LGRS.2026.3694123

Yifei Shao;Qizhou Gong;Yue Zhang;Caitang Sun;Shilong Wang;Xinhao Zhang;Yanzhang Wang;Quan Xu

{"title":"2.5-D Helicopter Transient Electromagnetic Data Inversion Method Based on SE-TransUNet","authors":"Yifei Shao;Qizhou Gong;Yue Zhang;Caitang Sun;Shilong Wang;Xinhao Zhang;Yanzhang Wang;Quan Xu","doi":"10.1109/LGRS.2026.3694123","DOIUrl":"https://doi.org/10.1109/LGRS.2026.3694123","url":null,"abstract":"Helicopter transient electromagnetic (HTEM) method exhibits significant advantages in geological exploration of complex terrain areas, yet its data inversion still faces challenges, such as low computational efficiency and poor inversion accuracy. Aiming at the problems of poor continuity of 1-D inversion and high computational cost of 3-D inversion, this letter proposes a 2.5-D HTEM inversion method based on the SE-TransUNet network. This method performs forward modeling on the subsurface resistivity model with infinite horizontal extension using a 3-D source to generate a dataset and uses an improved TransUNet architecture for inversion. The SE-TransUNet network combines the squeeze-and-excitation (SE) module and Transformer blocks on the basis of the U-Net neural network to enhance feature extraction and long-range dependency modeling, thereby improving the ability to reconstruct subsurface resistivity models. In experiments with synthetic and field data, compared with the Gauss–Newton method and the U-Net method, SE-TransUNet achieved better performance in the characterization of anomalous body boundaries, localization of anomalous bodies, and overall inversion accuracy, with an inversion time of only 5 s. The results verify the reliability and high efficiency of the proposed method for high-resolution HTEM inversion in practical geological exploration, providing an effective solution for fast and reliable HTEM inversion.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"23 ","pages":"7504405-7504405"},"PeriodicalIF":4.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148236265","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

OVDS-YOLO: An Efficient Detector for Oriented Vehicle Detection in SAR Images OVDS-YOLO：一种高效的SAR图像定向车辆检测检测器

IF 4.4

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2026-01-01 Epub Date: 2025-10-27 DOI: 10.1109/LGRS.2025.3625853

Yu Zhou;Bingting Zha;Zhen Zheng

{"title":"OVDS-YOLO: An Efficient Detector for Oriented Vehicle Detection in SAR Images","authors":"Yu Zhou;Bingting Zha;Zhen Zheng","doi":"10.1109/LGRS.2025.3625853","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3625853","url":null,"abstract":"Synthetic aperture radar (SAR) vehicle detection is of critical importance for military reconnaissance and traffic management. Compared to optical images, SAR images exhibit distinctive characteristics. Owing to significant interference from irrelevant objects and limited expressivity of vehicle features, most mainstream detectors based on deep learning face enormous challenges in SAR vehicle detection. Additionally, horizontal bounding boxes (HBBs) are replaced by oriented bounding boxes (OBBs) to suppress background noise and obtain more accurate boundaries of vehicles. However, angle regression remains a challenging problem. To address these issues, we propose an efficient detector for oriented vehicle detection in SAR images, called OVDS-you only look once (YOLO), combining three major improvements. Initially, in order to distinguish between target vehicles and clutter based on backgrounds, we introduce a background information aggregation module (BIAM), which effectively integrates global information and multi-scale surrounding environment information of vehicles. Furthermore, to guide the network to learn salient features of vehicles, we devise a feature selection module (FSM), which focuses on key features at the pixel and channel levels. Finally, to improve the accuracy of angle prediction, we embed a novel joint optimization paradigm (NJOP) into the detection framework, which performs dual-optimization for angles through a combination of Gaussian loss and angle loss. The experimental results on the constructed FARAD vehicle dataset demonstrate that the proposed method achieves excellent performance with low computational overhead.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"23 ","pages":"1-5"},"PeriodicalIF":4.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148479882","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

AFF-Net: Adaptive Frequency-Domain Fusion in a Dual-Stream Transformer With Wavelet-Assisted Alignment for Remote Sensing Image Segmentation 基于自适应频域融合的小波辅助双流变换器遥感图像分割

IF 4.4

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2026-01-01 Epub Date: 2026-03-20 DOI: 10.1109/LGRS.2026.3695313

Qingyang Ren;Tiancheng Ma;Leiguang Wang;Chen Zheng

{"title":"AFF-Net: Adaptive Frequency-Domain Fusion in a Dual-Stream Transformer With Wavelet-Assisted Alignment for Remote Sensing Image Segmentation","authors":"Qingyang Ren;Tiancheng Ma;Leiguang Wang;Chen Zheng","doi":"10.1109/LGRS.2026.3695313","DOIUrl":"https://doi.org/10.1109/LGRS.2026.3695313","url":null,"abstract":"Remote sensing semantic segmentation is essential for geoscientific analysis. Recently, although multimodal image fusion has achieved notable improvements over single-image approaches, existing methods still suffer from insufficient utilization of the observed information, especially in the alignment and fusion of multimodal detailed information. To address the above issues, a novel adaptive frequency-domain fusion network (AFF-Net) is proposed in this letter to achieve effective fusion of visible images (VISs) and digital surface model (DSM) data through a frequency-domain fusion (FDF) module, and to enhance the recognition capability of target boundaries via a semantic-guided detail alignment (SDA) strategy. In particular, the FDF module conducts an adaptive frequency-wise alignment and fusion scheme between VISs and DSM by decomposing them into low- and high-frequency subbands, enabling the model to capture more fine-grained complementary information across various frequency domains, rather than coarse-grained fusion in the spatial domain. The SDA strategy further restores high-frequency boundary details while suppressing noise to enable precise object delineation. AFF-Net was evaluated on the ISPRS Vaihingen and Potsdam datasets, achieving mIoU scores of 84.87% and 87.11%, as well as mF1 scores of 91.61% and 92.93%, respectively. These results show better performance over state-of-the-art (SOTA) methods and verify the effectiveness and superiority of the proposed method. The code is available at <uri>https://github.com/lanmao-create/AFF-Net</uri>","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"23 ","pages":"6011305-6011305"},"PeriodicalIF":4.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148489411","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

Moving Target Imaging via TR-SOKT for SAR With Maneuvering Trajectory 基于TR-SOKT的机动弹道SAR运动目标成像

IF 4.4

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2026-01-01 Epub Date: 2026-01-28 DOI: 10.1109/LGRS.2026.3657663

Yifan Wang;Shiyang Tang;Chenghao Jiang;Linrang Zhang;Jiahao Han;Zixuan He;Daobao Xu;Hongmeng Chen;Zhanye Chen

{"title":"Moving Target Imaging via TR-SOKT for SAR With Maneuvering Trajectory","authors":"Yifan Wang;Shiyang Tang;Chenghao Jiang;Linrang Zhang;Jiahao Han;Zixuan He;Daobao Xu;Hongmeng Chen;Zhanye Chen","doi":"10.1109/LGRS.2026.3657663","DOIUrl":"https://doi.org/10.1109/LGRS.2026.3657663","url":null,"abstract":"Maneuvers not only provide flexibility for synthetic aperture radar ground moving target imaging (SAR-GMTIm), but also means more complex echo characteristics, especially in high-resolution situations. This letter systematically analyzes and establishes an imaging model for SAR-GMTIm with maneuvering trajectory, revealing the high-order range cell migration, Doppler centroid ambiguity, and other issues. Based on above issues, an imaging method based on time reversal-second-order keystone transform (TR-SOKT) is proposed, which first corrects range walk migration (RWM) through time reversal (TR), then range curve migration (RCM) is corrected by SOKT, and azimuth residual phase is estimated and compensated by high-order ambiguity function (HAF). The proposed method is not affected by Doppler centroid ambiguity, making it suitable for target with various velocities. Additionally, parameter search is not performed during migration correction, resulting in low computational burden. The performance of the proposed method is verified by simulation and real data experiments.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"23 ","pages":"1-5"},"PeriodicalIF":4.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148488659","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

Gravity Anomaly Extraction Method Based on Dynamic Error Compensation of Undulated Flight 基于动态误差补偿的起伏飞行重力异常提取方法

IF 4.4

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2026-01-01 Epub Date: 2026-06-16 DOI: 10.1109/LGRS.2026.3704128

Shaokun Cai;Xiaoyu Qiu;Ruihang Yu;Bainan Yang;Kaixin Luo;Zhiming Xiong

引用次数: 0

YOLO-RSD: Enhanced Rotated Ship Detection in SAR Images YOLO-RSD：增强的SAR图像旋转船舶检测

IF 4.4

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2026-01-01 Epub Date: 2026-01-28 DOI: 10.1109/LGRS.2026.3657680

Ye Wu;Yuhu Shi;Hongyu Chen;Xingyu Hu;Xue Yang;Yang Cai;Yuxin Liu;Weiming Zeng

{"title":"YOLO-RSD: Enhanced Rotated Ship Detection in SAR Images","authors":"Ye Wu;Yuhu Shi;Hongyu Chen;Xingyu Hu;Xue Yang;Yang Cai;Yuxin Liu;Weiming Zeng","doi":"10.1109/LGRS.2026.3657680","DOIUrl":"https://doi.org/10.1109/LGRS.2026.3657680","url":null,"abstract":"Rotated ship detection in synthetic aperture radar (SAR) imagery remains challenging due to arbitrary orientations, large-scale variations, and strong sea clutter. This letter presents YOLO-RSD, a lightweight yet effective oriented object detector tailored for maritime surveillance. We first introduce a feature-aligned spatial attention (FASA) backbone that integrates deformable convolution with spatial attention to enhance orientation- and scale-adaptive feature extraction. Then, a FuseDiff neck module is designed to achieve consistent cross-scale semantic fusion via multibranch convolution and context diffusion. Finally, a task-aligned dynamic detection head (TDDH) is constructed using task-specific attention to reconcile classification–localization conflicts. Experiments on SSDD+ and RSDD-SAR datasets demonstrate that YOLO-RSD achieves the state-of-the-art performance with only 2.61 M parameters, outperforming recent YOLO variants and other comparative models in both accuracy and efficiency. The proposed framework offers a general and hardware-friendly paradigm for robust rotated target detection in SAR imagery.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"23 ","pages":"1-5"},"PeriodicalIF":4.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148489178","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

Evaluation of GPM DPR Precipitation Products After Orbit Boost Using Disdrometers in Italy 意大利轨道助推后GPM DPR降水产品的disdrometer评价

IF 4.4

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2026-01-01 Epub Date: 2025-11-28 DOI: 10.1109/LGRS.2025.3638692

Sabina Angeloni;Elisa Adirosi;Luca Baldini;Mario Montopoli;Alessandro Bracci;Vincenzo Capozzi;Clizia Annella;Simone Scapin;Paolo Valisa;Lorenzo Luini;Roberto Nebuloni;Roberto Cremonini;Renzo Bechini;David B. Wolff

{"title":"Evaluation of GPM DPR Precipitation Products After Orbit Boost Using Disdrometers in Italy","authors":"Sabina Angeloni;Elisa Adirosi;Luca Baldini;Mario Montopoli;Alessandro Bracci;Vincenzo Capozzi;Clizia Annella;Simone Scapin;Paolo Valisa;Lorenzo Luini;Roberto Nebuloni;Roberto Cremonini;Renzo Bechini;David B. Wolff","doi":"10.1109/LGRS.2025.3638692","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3638692","url":null,"abstract":"Precipitation measurements are crucial for improving climate models and weather forecasting, with satellite-based radar systems, such as the dual precipitation radar (DPR) on board the Global Precipitation Measurement Core Observatory (GPM CO), playing a pivotal role in enhancing observational coverage, especially in remote regions. In November 2023, an orbit boost maneuver raised the GPM CO’s altitude from 400 to 435 km, potentially influencing the quality of DPR data. This study evaluates the impact of this altitude increase on the accuracy of DPR-derived precipitation products by comparing data acquired before and after the orbit boost. We assess the performance of DPR measurements against ground-based laser disdrometer data collected in Italy between May 2018 and November 2024. The results highlight the effects of the altitude change on rainfall retrievals, contributing to a better understanding of how orbital adjustments can affect satellite-based precipitation observations.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"23 ","pages":"1-5"},"PeriodicalIF":4.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11271247","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148489333","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MBE-Net: A Multibranch Edge-Guided Feature Enhancement Algorithm for SAR Ship Detection MBE-Net：一种多分支边缘引导的SAR舰船检测特征增强算法

IF 4.4

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2026-01-01 Epub Date: 2026-03-09 DOI: 10.1109/LGRS.2026.3672324

Jinwei Wang;Zihao Wang;Guangcai Sun;Bo Zhao

{"title":"MBE-Net: A Multibranch Edge-Guided Feature Enhancement Algorithm for SAR Ship Detection","authors":"Jinwei Wang;Zihao Wang;Guangcai Sun;Bo Zhao","doi":"10.1109/LGRS.2026.3672324","DOIUrl":"https://doi.org/10.1109/LGRS.2026.3672324","url":null,"abstract":"Synthetic aperture radar (SAR) imagery plays a vital role in maritime surveillance, yet it faces severe challenges such as speckle noise, cluttered backgrounds, and the small scale and low contrast of ship targets. To overcome these issues, we propose MBE-Net, a lightweight and high-accuracy SAR ship detection network featuring multibranch edge-guided feature enhancement. First, the edge-enhanced wavelet backbone (EEWB) is designed to enhance edge information and suppress detail loss by integrating multibranch edge guidance and wavelet pooling, thereby improving small-ship perception in complex scenes. Second, the lightweight decoupled detail-enhanced head (LD-DEH) decouples classification and regression tasks while embedding a detail-enhanced convolution module to recover high-resolution features, significantly boosting localization precision under strong interference. Finally, the adaptive contextual focus feature pyramid network (ACF-FPN) introduces context-guided fusion to improve multiscale feature alignment and robustness, especially in low-contrast and cluttered environments. Extensive experiments on the HRSID and SSDD datasets validate that MBE-Net achieves excellent detection accuracy with minimal computational cost, making it highly suitable for real-time SAR ship detection.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"23 ","pages":"1-5"},"PeriodicalIF":4.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148489384","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 Registration of Multimodal Remote Sensing Images Using Steerable Hermite Filters and Geometric Constraints 基于可操纵Hermite滤波器和几何约束的多模态遥感图像鲁棒配准

IF 4.4

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2026-01-01 Epub Date: 2025-12-17 DOI: 10.1109/LGRS.2025.3645106

Ruixiang Li;Mi Wang;Jun Pan;Yingdong Pi

{"title":"Robust Registration of Multimodal Remote Sensing Images Using Steerable Hermite Filters and Geometric Constraints","authors":"Ruixiang Li;Mi Wang;Jun Pan;Yingdong Pi","doi":"10.1109/LGRS.2025.3645106","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3645106","url":null,"abstract":"Co-registration of multimodal remote sensing images (MRSIs) remains a challenging task due to nonlinear radiometric differences (NRD) and geometric distortions. This letter proposes a robust registration method based on steerable Hermite filters and geometric constraints. The method consists of three stages: coarse registration, image matching, and fine registration. In the first stage, geo-referencing information is used to partition the overlapping region into subblocks for initial alignment. In the second stage, steerable Hermite filters are applied to extract multiscale and multiorientation structural features, which are fused into a principal moment map to detect highly repeatable feature points. A structural descriptor is subsequently constructed from an orientation index map (OIM) generated through hierarchical pooling, enabling robust matching under NRD. In the final stage, an iterative optimization strategy incorporating geometric constraints is employed to eliminate mismatches and refine the registration. Experimental results on multiple MRSI datasets demonstrate that the proposed method outperforms state-of-the-art approaches in terms of both matching robustness and registration accuracy.","PeriodicalId":91017,"journal":{"name":"IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society","volume":"23 ","pages":"1-5"},"PeriodicalIF":4.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148489751","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