IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society最新文献_第6页

DIP-MoG: Non-i.i.d. Seismic Noise Attenuation Using Mixture of Gaussians Noise Model and Deep Image Prior DIP-MoG: Non-i.i.d。基于高斯噪声模型和深度图像先验的地震噪声抑制

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-04-15 DOI: 10.1109/LGRS.2025.3560978

Yuqing Wang;Jiangjun Peng;Bangyu Wu;Bo Li

{"title":"DIP-MoG: Non-i.i.d. Seismic Noise Attenuation Using Mixture of Gaussians Noise Model and Deep Image Prior","authors":"Yuqing Wang;Jiangjun Peng;Bangyu Wu;Bo Li","doi":"10.1109/LGRS.2025.3560978","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3560978","url":null,"abstract":"Seismic data denoising is essential for subsequent inversion and interpretation tasks. However, most existing methods rely on loss functions, which assume that seismic noise follows an independent and identically distributed (i.i.d.) Gaussian distribution, which does not align with the characteristics of actual seismic noise. In this letter, we first analyze the principle of the <inline-formula> <tex-math>$L_{2}$ </tex-math></inline-formula>-norm loss function in suppressing i.i.d. Gaussian noise from the maximum a posteriori (MAP) perspective and then introduce the Mixture of Gaussians (MoGs) model to handle non-i.i.d. noise suppression. In addition, we optimize the MoG model using the expectation-maximization (EM) algorithm for improved performance. We propose a novel approach, DIP-MoG, which integrates the deep image prior (DIP) with the MoG model for enhanced denoising. To validate the performance of DIP-MoG, we conduct experiments on two synthetic datasets contaminated with an MoG noise and field noise, as well as a field seismic dataset. The results from both synthetic and field data demonstrate the superior denoising performance of DIP-MoG.","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":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143896501","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

1-D Mirrored Aperture Synthesis Based on Artificial Magnetic Conductor 基于人工磁导体的一维镜像孔径合成

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-04-15 DOI: 10.1109/LGRS.2025.3561129

Rigeng Wu;Chengwang Xiao;Zhenyu Lei;Jian Dong;Yue Zhang

{"title":"1-D Mirrored Aperture Synthesis Based on Artificial Magnetic Conductor","authors":"Rigeng Wu;Chengwang Xiao;Zhenyu Lei;Jian Dong;Yue Zhang","doi":"10.1109/LGRS.2025.3561129","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3561129","url":null,"abstract":"In 1-D mirrored aperture synthesis (MAS), the antenna array arrangement and metal reflector are crucial in determining the rank of the transformation matrix. Accurate cosine visibility is achievable only when the transformation matrix is full rank. However, the anti-phase characteristic of the metal reflector introduces nonzero elements of “−1” into the matrix, leading to rank deficiency (RD). This letter proposes a method of using artificial magnetic conductor (AMC) with in-phase reflection property instead of metal reflector to ensure that the transformation matrix only contains nonzero elements “1.” Based on this property, the rank of both linear and nonlinear arrays is verified. The results indicate that AMC can effectively enhance the rank of the transformation matrix, potentially achieving full rank. Additionally, further verification is performed on the reconstruction of trapezoidal extended source scene using two types of arrays. The results demonstrate that AMC-based 1-D MAS can achieve a low root-mean-square error (RMSE), significantly improving the quality of the reconstructed 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":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143875140","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 Sonar Image Targets Detection Based on Improved RT-DETR 基于改进RT-DETR的水下声纳图像目标检测

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-04-15 DOI: 10.1109/LGRS.2025.3560769

Ang Li;Raseeda Hamzah;Yousheng Gao

{"title":"Underwater Sonar Image Targets Detection Based on Improved RT-DETR","authors":"Ang Li;Raseeda Hamzah;Yousheng Gao","doi":"10.1109/LGRS.2025.3560769","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3560769","url":null,"abstract":"Underwater sonar imagery is characterized by small target sizes and low resolution, which can result in detection failures or false positives. To counteract these challenges, we introduce the underwater sonar detection transformer (US-DETR), an underwater sonar object detection model derived from the real-time detection transformer (RT-DETR) framework, incorporating attention-based feature fusion. US-DETR includes a novel enhanced feature interaction (EFI) module, which enhances the feature extraction network’s ability to perceive global information of the detected target. In addition, we propose a novel nonlocal attention feature fusion (NAFF) module to heighten the network’s sensitivity to the spatial relationships between feature channels across different scales, thereby enhancing its channel position and global information awareness. Experiments are conducted on a benchmark underwater sonar image dataset. Experimental results show that compared with RT-DETR, US-DETR achieves a 2.2% higher mean average precision (mAP) and a 2.1% higher <inline-formula> <tex-math>$F1$ </tex-math></inline-formula> score compared with RT-DETR. The model also strikes an effective balance between detection speed and accuracy, achieving real-time performance of 126 FPS, which can meet the real-time requirements in industrial production.","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":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143892422","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

DFDNet: Deep Feature Decoupling for Oriented Object Detection 面向对象检测的深度特征解耦

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-04-14 DOI: 10.1109/LGRS.2025.3560388

Yuhan Sun;Shengyang Li

{"title":"DFDNet: Deep Feature Decoupling for Oriented Object Detection","authors":"Yuhan Sun;Shengyang Li","doi":"10.1109/LGRS.2025.3560388","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3560388","url":null,"abstract":"Objects in remote sensing images exhibit diverse orientations. Current oriented object detection (OOD) methods estimate the object angle by designing different loss functions and bounding box representations. However, these approaches do not account for the effects of coupling between rotation-equivariant and -invariant features on the regression of oriented bounding box (OBB) parameters. We manifest the problem in two aspects: 1) the coupling of parameters with different attributes. Current OOD methods overlook the inherent differences among features representing an object’s location, scale, and angle, making it challenging to accurately predict the OBB parameters with different attributes and 2) the coupling of object and background features. Conventional OOD methods apply convolution kernels uniformly across objects and background regions, leading to feature entanglement and degradation in detection performance. To address the above issues, we propose a deep feature decoupling network (DFDNet) to decouple the extracted features. Specifically, we propose parameter regression decoupling (PRD) to separate feature maps based on their attributes, subsequently assigning them to distinct branches for the OBB parameter regression. This approach ensures the decoupling of features related to an object’s location, shape, angle, and category. Additionally, to enhance the ability of OOD networks to differentiate between object and background features, we designed the mask reinforcement module (MRM), which is integrated into the PRD branches. The MRM dynamically adjusts the weights of object features, suppressing background interference and enhancing the distinction between object and background features. Extensive experiments conducted on the DOTA, HRSC2016, and UCAS-AOD datasets validate the effectiveness of DFDNet, demonstrating that it achieves state-of-the-art performance.","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":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143879498","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

Frequency-Aware Contextual Feature Pyramid Network for Infrared Small-Target Detection 红外小目标检测的频率感知上下文特征金字塔网络

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-04-14 DOI: 10.1109/LGRS.2025.3560340

Shu Cai;Jinfu Yang;Tao Xiang;Jinglei Bai

{"title":"Frequency-Aware Contextual Feature Pyramid Network for Infrared Small-Target Detection","authors":"Shu Cai;Jinfu Yang;Tao Xiang;Jinglei Bai","doi":"10.1109/LGRS.2025.3560340","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3560340","url":null,"abstract":"Due to the absence of detailed information, such as texture, shape, and color, detecting infrared small targets remains a challenging problem. While existing model-driven and data-driven approaches have made some progress, they still struggle to effectively exploit global contextual information and frequency-specific details. In this letter, we introduce a frequency-aware contextual feature pyramid network (FACFPNet) to address these limitations in infrared small-target detection. Specifically, we first estimate the correlation between high- and low-frequency feature representations within an encoder-decoder framework based on the ResNet-18 backbone. This is achieved through the contextual fine-grained block (CFGB), which effectively combines local fine-grained features with global semantic information for enhanced contextual feature modeling. Next, we propose a frequency-aware attention module (FAAM) to address the underutilization of prior frequency knowledge in infrared small targets, thereby improving the preservation of these features. This module enhances global contextual representation by more effectively extracting high- and low-frequency information. Finally, during the decoding stage, shallow fine-structure information is interactively fused with deep semantic features through the asymmetric enhancement fusion module (AEFM), which strengthens the representation of small targets and improves information retention. Experimental results on three publicly available datasets, SIRST-Aug, MdvsFA, and IRSTD-1K, demonstrate that our method achieves superior detection performance.","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":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143883474","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

Weak Seafloor Echo Detection for Airborne LiDAR Bathymetry Considering Waveform Feature Confusion 考虑波形特征混淆的机载激光雷达测深微弱海底回波检测

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-04-14 DOI: 10.1109/LGRS.2025.3560328

Yadong Guo;Wenxue Xu;Yanxiong Liu;Yikai Feng;Fanlin Yang;Long Yang;Zhen Guo

{"title":"Weak Seafloor Echo Detection for Airborne LiDAR Bathymetry Considering Waveform Feature Confusion","authors":"Yadong Guo;Wenxue Xu;Yanxiong Liu;Yikai Feng;Fanlin Yang;Long Yang;Zhen Guo","doi":"10.1109/LGRS.2025.3560328","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3560328","url":null,"abstract":"Full-waveform airborne LiDAR bathymetry (ALB), which provides waveforms and point clouds, has become an essential technology for shallow water surveys. However, weak seafloor echoes are challenging to detect accurately because of waveform feature confusion caused by the complex measurement environments. To address this issue, waveform feature importances, feature histograms, and feature spaces of 14-D waveform features are conducted to analyze the waveform feature confusion. Then, a random forest with optimized thresholds (RFOTs) is proposed to detect normal seafloor echoes and weak seafloor echoes. Finally, waveform sharpening and condition screening are used to extract the seafloor echoes for overlapping waveforms in very shallow waters. The proposed method was verified with 14 swaths obtained by the Optech Aquarius system around Wuzhizhou Island. The results show that the energy features (area under curve, amplitude, etc.) can better discriminate the difference between weak seafloor echoes and noise than the shape features (RL area ratio, kurtosis, etc.). The number of seafloor echoes detected by the proposed method increased by 148.86% compared with the Aquarius system. The reference data prove that seafloor points detected by the proposed method are accurate and effective. Thus, this contribution effectively improves the bathymetric performance of the ALB system.","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":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143892551","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

Attention-Driven Object Encoding and Multiscale Contextual Perception for Improved Cross-View Object Geo-Localization 注意驱动目标编码和多尺度上下文感知改进跨视目标地理定位

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-04-14 DOI: 10.1109/LGRS.2025.3560258

Haoshuai Song;Xiaochong Tong;Xiaoyu Zhang;Yaxian Lei;He Li;Congzhou Guo

{"title":"Attention-Driven Object Encoding and Multiscale Contextual Perception for Improved Cross-View Object Geo-Localization","authors":"Haoshuai Song;Xiaochong Tong;Xiaoyu Zhang;Yaxian Lei;He Li;Congzhou Guo","doi":"10.1109/LGRS.2025.3560258","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3560258","url":null,"abstract":"Cross-view object geo-localization (CVOGL) is essential for applications like navigation and intelligent city management. By identifying objects in street-view/drone-view and precisely locating them in satellite imagery, more accurate geo-localization can be achieved compared to retrieval-based methods. However, existing approaches fail to account for query object shape/size and significant scale variations in remote sensing images. To address these limitations, we propose an attention-driven multiscale perception network (AMPNet) for cross-view geo-localization. AMPNet employs an attention-driven object encoding (ADOE) based on segmentation, which provides prior information to enable learning more discriminative representations of the query object. In addition, AMPNet introduces a cross-view multiscale perception (CVMSP) module that captures multiscale contextual information using varying convolution kernels, and applies an MLP to enhance channel-wise feature interactions. Experimental results demonstrate that AMPNet outperforms state-of-the-art methods in both ground-to-satellite and drone-to-satellite object localization tasks on a challenging dataset.","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":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143883518","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

Coastal Performance of Sentinel-6MF New High-Resolution Wet Tropospheric Correction Sentinel-6MF新型高分辨率湿对流层校正的海岸性能

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-04-11 DOI: 10.1109/LGRS.2025.3560196

Telmo Vieira;Pedro Aguiar;Clara Lázaro;M. Joana Fernandes

{"title":"Coastal Performance of Sentinel-6MF New High-Resolution Wet Tropospheric Correction","authors":"Telmo Vieira;Pedro Aguiar;Clara Lázaro;M. Joana Fernandes","doi":"10.1109/LGRS.2025.3560196","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3560196","url":null,"abstract":"Sentinel-6 Michael Freilich (S6MF) satellite carries the Advanced Microwave Radiometer for Climate (AMR-C), which, in addition to the standard low frequency channels, includes a High-Resolution Microwave Radiometer (HRMR) with channels at 90, 130, and 166 GHz. This subsystem allows higher spatial resolution for enhanced Wet Tropospheric Correction (WTC) measurements in coastal zones. The current S6MF products provide two different WTC fields: AMR WTC, computed from AMR measurements alone, and RAD WTC, computed from the combination of AMR and HRMR. The aim of this study is to evaluate this new high-resolution WTC from S6MF, over the global coastal regions, during the first three years of the mission (2021–2023), in particular to quantify the performance of the RAD WTC when compared with the AMR WTC. Results show that, on average, for distances to coast in the range of 0–5 km, RAD WTC is only available in 13% of S6MF points and an inter-comparison between these two corrections reveals the largest differences for the range of distances to land between 5 and 10 km. Comparisons with ERA5 and global navigation satellite systems (GNSS) reveal that the new RAD WTC is better than the AMR WTC for distances to coast in the range of 5–20 km and, over open-ocean, the current algorithms do not take advantage of the high frequency channels. This evaluation shows how radiometers with high-resolution channels such as the one deployed in S6MF improve the WTC retrieval for 5–20 km from the coast, allowing a higher recovery of accurate sea level measurements in these regions.","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":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143865330","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

Layered Media Parameter Estimation Based on Hyperbolic Fitting in GPR B-Scan 基于双曲拟合的GPR b扫描分层介质参数估计

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-04-11 DOI: 10.1109/LGRS.2025.3560177

Tian Lan;Xitao Sun;Xiaopeng Yang;Junbo Gong;Xueyao Hu

引用次数: 0

Radar Waveform Sequence Design for PSL Optimization via Iterative Neural Network 基于迭代神经网络的PSL优化雷达波形序列设计

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-04-11 DOI: 10.1109/LGRS.2025.3560073

Yuxin Yan;Yifeng Wu;Lei Zhang

引用次数: 0