IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society最新文献_第8页

A Novel Size-Aware Local Contrast Measure for Tiny Infrared Target Detection

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-02-25 DOI: 10.1109/LGRS.2025.3542219

Lihao Ye;Jing Liu;Jianting Zhang;Jiayi Ju;Yuan Wang

{"title":"A Novel Size-Aware Local Contrast Measure for Tiny Infrared Target Detection","authors":"Lihao Ye;Jing Liu;Jianting Zhang;Jiayi Ju;Yuan Wang","doi":"10.1109/LGRS.2025.3542219","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3542219","url":null,"abstract":"Detecting tiny infrared (IR) targets in diverse complex backgrounds faces many challenges, e.g., extremely few features of the tiny targets, cluttered backgrounds, and interferences from surrounding similar objects. In this letter, we propose a novel size-aware local contrast measure (SALCM) method to detect tiny IR targets. First, to tackle the problem of extremely few features, various local features are extracted through monogenic signal decomposition, which can effectively enrich the potential features of the tiny targets. Second, the Canny detector is used to precisely delineate the contours of multiple candidate targets in the fused image to estimate the exact shapes and sizes of candidate targets. This ensures that the proposed method adapts to both tiny targets and small targets (with relatively larger sizes). Finally, local contrast enhancement is used to highlight the target regions while suppressing the background clutters and interferences from surrounding similar objects, leading to accurate detection. The experimental results on six real IR target datasets demonstrate the superiority of the proposed method in terms of target enhancement, background suppression, and detection accuracy, for detecting IR targets of various sizes.","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":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143512881","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

Underwater Optical Image Contrast Enhancement via Color Channel Matching

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-02-25 DOI: 10.1109/LGRS.2025.3545175

Xiaoguo Chen;Shilong Sun;Yaqi Gao;Wenyi Zhao;Weidong Zhang

{"title":"Underwater Optical Image Contrast Enhancement via Color Channel Matching","authors":"Xiaoguo Chen;Shilong Sun;Yaqi Gao;Wenyi Zhao;Weidong Zhang","doi":"10.1109/LGRS.2025.3545175","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3545175","url":null,"abstract":"Due to the complex physical environment underwater, underwater captured images often suffer from issues such as color distortion, low contrast, and loss of texture details. To address this issue, we propose a color channel matching (CCM) method for underwater optical image contrast enhancement, called CCM. Specifically, we first convert the raw image into a grayscale image and employ histogram matching techniques to make the brightness distribution of the image more uniform, thereby reducing brightness variations caused by environmental factors. Then, we transform the matched image into the hue-saturation-intensity (HSI) color space and optimize the HSI channels separately. During this process, we decouple the intensity information from the color information to avoid interference during enhancement, which employs adaptive histogram equalization on the intensity channel to improve contrast and detailed representation further. Finally, we fuse the processed intensity channel with the optimized hue and saturation channels to obtain the final contrast-enhanced image. Extensive qualitative and quantitative experimental results demonstrate that the proposed method exhibits strong robustness and generalization capabilities in enhancing the contrast of underwater images.","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":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143580877","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

CDME: Convolutional Dictionary Iterative Model for Pansharpening With Mixture of Experts

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-02-25 DOI: 10.1109/LGRS.2025.3545472

Zixu Li;Ying Gao;Genji Yuan;Zhen Hua;Jinjiang Li

{"title":"CDME: Convolutional Dictionary Iterative Model for Pansharpening With Mixture of Experts","authors":"Zixu Li;Ying Gao;Genji Yuan;Zhen Hua;Jinjiang Li","doi":"10.1109/LGRS.2025.3545472","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3545472","url":null,"abstract":"In this letter, we propose a convolutional dictionary iterative model for pansharpening with a mixture of experts. First, we define an observation model to model the common and unique feature information between multispectral (MS) and panchromatic (PAN) images. During this process, a proximal gradient algorithm is used to iteratively update the network parameters. The adaptive expert module (AEM) is designed to handle the unique and common features separately by using PAN mixture of experts (PMOE), multispectral mixture-of-experts (MMOE), and common mixture-of-experts (CMOE) modules, to achieve effective information reconstruction. Finally, the expert mixture fusion module (EMFM) adaptively integrates the information from the three mixture-of-experts (MOE) components by dynamically adjusting their respective weights, resulting in the final fused image. We conducted full-resolution and reduce-resolution experiments on GF2 and WV3 datasets with current state-of-the-art methods, and the experimental results show that our method performs best. The code is released on <uri>https://github.com/who15/CDME</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":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143688113","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

UNMamba: Cascaded Spatial–Spectral Mamba for Blind Hyperspectral Unmixing

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-02-25 DOI: 10.1109/LGRS.2025.3545505

Dong Chen;Junping Zhang;Jiaxin Li

{"title":"UNMamba: Cascaded Spatial–Spectral Mamba for Blind Hyperspectral Unmixing","authors":"Dong Chen;Junping Zhang;Jiaxin Li","doi":"10.1109/LGRS.2025.3545505","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3545505","url":null,"abstract":"Blind hyperspectral unmixing (HU) has advanced significantly with the emergence of deep learning-based methods. However, the localized operations of convolutional neural networks (CNNs) and the high computational demands of Transformers present challenges for blind HU. This necessitates the development of image-level unmixing methods capable of capturing long-range spatial-spectral dependencies with low computational demands. This letter proposes a cascaded spatial-spectral Mamba model, termed UNMamba, which leverages the strengths of Mamba to efficiently model long-range spatial-spectral dependencies with linear computational complexity, achieving superior image-level unmixing performance with small parameters and operations. Specifically, UNMamba first captures long-range spatial dependencies, followed by the extraction of global spectral features, forming long-range spatial-spectral dependencies, which are subsequently mapped into abundance maps. Then, the input image is reconstructed using the linear mixing model (LMM), incorporating weighted averages of multiple trainable random sequences and an endmember loss to learn endmembers. UNMamba is the first unmixing approach that introduces the state-space models (SSMs). Extensive experimental results demonstrate that, without relying on any endmember initialization techniques [such as vertex component analysis (VCA)], the proposed UNMamba achieves significantly high unmixing accuracy, outperforming state-of-the-art methods. Codes are available at <uri>https://github.com/Preston-Dong/UNMamba</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":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143564101","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

Significant Elastic Ground Deformations After the January 15, 2022 Hunga-Tonga Eruption

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-02-25 DOI: 10.1109/LGRS.2025.3545686

Long Tang;Wu Chen;Kai Zheng;Pan Li

引用次数: 0

CSEM Data Denoising Based on STL-LPFMD 基于 STL-LPFMD 的 CSEM 数据去噪

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-02-24 DOI: 10.1109/LGRS.2025.3544658

Zijie Liu;Yanfang Hu;Diquan Li

引用次数: 0

SRODET: Semi-Supervised Remote Sensing Object Detection With Dynamic Pseudo-Labeling

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-02-24 DOI: 10.1109/LGRS.2025.3544807

Wenyong Wang;Yuanzheng Cai;Tao Wang

引用次数: 0

Parcel-Level Mapping of Artificial Forests Along the Middle Reach Valley of Yarlung Tsangpo River Based on Deep Learning Algorithms

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-02-20 DOI: 10.1109/LGRS.2025.3543344

Changshuo Xia;Wei Zhao;Jianbo Tan;Tianjun Wu;Tao Ding

{"title":"Parcel-Level Mapping of Artificial Forests Along the Middle Reach Valley of Yarlung Tsangpo River Based on Deep Learning Algorithms","authors":"Changshuo Xia;Wei Zhao;Jianbo Tan;Tianjun Wu;Tao Ding","doi":"10.1109/LGRS.2025.3543344","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3543344","url":null,"abstract":"Artificial forest (AF) is an effective means of human intervention in forest ecosystems, aiming at preventing issues, such as soil erosion and land desertification. However, owing to the characteristics of large-scale afforestation projects, which often involve vast spatial extents and extended temporal scales, AF usually exhibits complex distribution patterns. In such cases, traditional remote sensing methods usually fail to accurately monitor AF conditions. To address this issue, this study introduced deep learning (DL) algorithms to extract multilevel features from remote sensing images for AF mapping and employed image processing techniques to enhance AF boundary determination. Through integrating these two approaches, high-resolution mapping of AF parcels was generated for a typical region in the middle reach valley of the Yarlung Tsangpo River. In the validation phase, the extracted regions were compared with manually labeled datasets and three accuracy metrics were calculated to demonstrate the extraction performance of the model. The accuracy reached 90.12% with the intersection over union (IoU) of 88.42%, and the cross-entropy loss function is only 0.0218. Meanwhile, three sampling areas with different coverages were selected for comparison, and the extractions have better performance than the SAM model based on the comparison with the samples. The findings reveal that this method can segment each AF parcel into independent objects, and the results would be helpful for parcel-based researches.","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":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570684","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

Parallel Perceptual Attention and Multifrequency Matching Network for UAV Tracking

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-02-20 DOI: 10.1109/LGRS.2025.3543825

Anping Deng;Guangliang Han;Hang Yang;Zhichao Liu;Minglu Li;Dianbing Chen

{"title":"Parallel Perceptual Attention and Multifrequency Matching Network for UAV Tracking","authors":"Anping Deng;Guangliang Han;Hang Yang;Zhichao Liu;Minglu Li;Dianbing Chen","doi":"10.1109/LGRS.2025.3543825","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3543825","url":null,"abstract":"In recent years, trackers based on deep learning have demonstrated immense potential, attributed to their robust modeling capabilities. Nevertheless, due to the limited computational power of uncrewed aerial vehicle (UAV) platforms and the complexity of scenarios encountered during tracking, the existing trackers struggle to effectively balance algorithmic accuracy and operational speed. This defect seriously affects the practical significance of tracking algorithm based on deep learning. To address this challenge, we have devised PMTrack, an efficient tracking model grounded in Siamese neural networks. The key innovations of PMTrack encompass: 1) the adoption of parallel perceptual attention (PPA) to enhance feature saliency and 2) the design of a multifrequency matching (MFM) network that facilitates feature matching through multidimensional feature information while mitigating redundant computations. PMTrack is both efficient and effective: its effectiveness is validated through comprehensive evaluations on multiple public benchmarks. We have deployed PMTrack on various drone platforms, specifically running at a speed of 46 frames per second (FPS) on the typical embedded aerial tracking platform Nvidia Xavier. This confirms the feasibility and practicality of the algorithm presented in this letter in real world.","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":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570619","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

Learning Robust Feature Representation for Cross-View Image Geo-Localization

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-02-20 DOI: 10.1109/LGRS.2025.3543949

Wenjian Gan;Yang Zhou;Xiaofei Hu;Luying Zhao;Gaoshuang Huang;Mingbo Hou

{"title":"Learning Robust Feature Representation for Cross-View Image Geo-Localization","authors":"Wenjian Gan;Yang Zhou;Xiaofei Hu;Luying Zhao;Gaoshuang Huang;Mingbo Hou","doi":"10.1109/LGRS.2025.3543949","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3543949","url":null,"abstract":"The cross-view image geo-localization (CVGL) refers to determining the geographic location of a given query image using an image database with the known location information. Existing methods mainly focus on learning discriminative image representations to optimize the distance of image feature representations in feature space without fully considering the positional relation information of the features and the information redundancy in the features themselves. Therefore, we proposed a cross-view image localization method that combines the global spatial relation attention (GSRA) with feature aggregation. First, we utilize the lightweight GSRA to learn the spatial location structure information of features, which fully enhances the perceptual and discriminative capabilities of the model. The proposed attention has a little effect on the complexity and memory occupancy of the model and can be generalized to other image-processing tasks. In addition, we introduce the sinkhorn algorithm for locally aggregated descriptors (SALADs), which represents the aggregation of local features as an optimal transport problem and selectively discards useless information during the clustering and assignment of features, thus enhancing the generalization and robustness of the descriptors. Experimental results on the public University-1652, CVACT, and CVUSA datasets validate the effectiveness and superiority of the proposed method. Our code is available at: <uri>https://github.com/WenjianGan/LRFR</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":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143570873","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