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

Erratum to “A Deep Learning Solution for Phase Screen Estimation in SAR Tomography” 对“SAR断层扫描相位屏估计的深度学习解决方案”的勘误

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-07-17 DOI: 10.1109/LGRS.2025.3580932

Hossein Aghababaei;Giampaolo Ferraioli;Sergio Vitale;Alfred Stein

引用次数: 0

Focusing Hypersonic Vehicle-Borne SAR Data With Spiral Trajectory Based on 2-D Lagrange Interpolation 基于二维拉格朗日插值的高超声速机载SAR数据螺旋轨迹聚焦

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-07-17 DOI: 10.1109/LGRS.2025.3590055

Zixuan He;Shiyang Tang;Chenghao Jiang;Zhanye Chen;Ping Guo;Linrang Zhang;Xintian Zhang;Fan Xu;Yifan Wang

引用次数: 0

Mask-Guided and Confidence-Driven Unsupervised Domain Adaptation for Hyperspectral Cross-Scene Classification 基于掩模制导和置信度驱动的无监督域自适应高光谱跨场景分类

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-07-16 DOI: 10.1109/LGRS.2025.3589677

Ying Cui;Longyu Zhu;Liguo Wang;Shan Gao;Chunhui Zhao

{"title":"Mask-Guided and Confidence-Driven Unsupervised Domain Adaptation for Hyperspectral Cross-Scene Classification","authors":"Ying Cui;Longyu Zhu;Liguo Wang;Shan Gao;Chunhui Zhao","doi":"10.1109/LGRS.2025.3589677","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3589677","url":null,"abstract":"Hyperspectral image (HSI) classification holds great potential for practical applications, but its widespread adoption is limited by the high cost of manual annotation. While unsupervised domain adaptation (UDA) offers a solution by transferring knowledge from labeled source domains (SDs) to unlabeled target domains (TDs), existing methods primarily focus on statistical-level distribution alignment, neglecting instance-level variations in TD data. In addition, for the interfering information such as noise and redundancy that are prevalent in HSI, there are few methods to consider processing the original data at the point level. To overcome these limitations, we propose a mask-guided and confidence-driven UDA (MCUDA) method. It introduces point-level learnable masks to dynamically optimize the input HSI data cube, effectively suppressing interference and enhancing domain-invariant feature extraction. It also proposes a pseudolabel sample set generation strategy based on the idea of confident learning, which takes into account the instance-level differences and domain-related information of TD data. Comprehensive experiments on two cross-scene datasets demonstrate that MCUDA outperforms existing UDA methods, achieving superior classification accuracy.","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":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144705045","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

LoRA-CLIP: Efficient Low-Rank Adaptation of Large CLIP Foundation Model for Scene Classification LoRA-CLIP：基于大CLIP基础模型的高效低秩自适应场景分类

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-07-16 DOI: 10.1109/LGRS.2025.3589738

Mohamad Mahmoud Al Rahhal;Yakoub Bazi;Mansour Zuair

{"title":"LoRA-CLIP: Efficient Low-Rank Adaptation of Large CLIP Foundation Model for Scene Classification","authors":"Mohamad Mahmoud Al Rahhal;Yakoub Bazi;Mansour Zuair","doi":"10.1109/LGRS.2025.3589738","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3589738","url":null,"abstract":"Scene classification in optical remote sensing (RS) imagery has been extensively investigated using both learning-from-scratch approaches and fine-tuning of ImageNet pretrained models. Meanwhile, contrastive language-image pretraining (CLIP) has emerged as a powerful foundation model for vision-language tasks, demonstrating remarkable zero-shot capabilities across various domains. Its image encoder is a key component in many vision instruction-tuning models, enabling effective alignment of text and visual modalities for diverse tasks. However, its potential for supervised RS scene classification remains unexplored. This work investigates the efficient adaptation of large CLIP models (containing over 300 M parameters) through low-rank adaptation (LoRA), specifically targeting the attention layers. By applying LoRA to CLIP’s attention mechanisms, we can effectively adapt the vision model for specialized scene classification tasks with minimal computational overhead, requiring fewer training epochs than traditional fine-tuning methods. Our extensive experiments demonstrate the promising capabilities of LoRA-CLIP. By training only on a small set of additional parameters, LoRA-CLIP outperforms models pretrained on ImageNet, demonstrating the clear advantages of using image–text pretrained backbones for scene classification.","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":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144695551","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

Bathymetric Prediction and Uncertainty Quantification Using a Bayesian Deep Neural Network Based on Gravity Data 基于重力数据的贝叶斯深度神经网络测深预测与不确定性量化

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-07-15 DOI: 10.1109/LGRS.2025.3589454

Jiuqiang Yang;Chenguang Liu;Yanliang Pei;Pengyao Zhi;Niantian Lin;Long Ma

{"title":"Bathymetric Prediction and Uncertainty Quantification Using a Bayesian Deep Neural Network Based on Gravity Data","authors":"Jiuqiang Yang;Chenguang Liu;Yanliang Pei;Pengyao Zhi;Niantian Lin;Long Ma","doi":"10.1109/LGRS.2025.3589454","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3589454","url":null,"abstract":"As seabed topography is closely related to the ocean gravity field, utilizing gravity data for seabed topography inversion has become the mainstream method. Although conventional deep neural network (DNN) methods have great potential in bathymetric prediction, they can neither evaluate the uncertainty of the prediction process nor the impact of uncertainty on prediction results, which limits their practical application value. To address this problem, a Bayesian DNN (BDNN) method is proposed for bathymetric prediction and uncertainty quantification. This method introduces Monte Carlo (MC) dropout variational inference into the architecture of a conventional DNN. Thus, the model achieves uncertainty quantification of prediction results with only a small amount of network structure changes. In addition, the captured uncertainty is fed back into the network training process to constrain the model parameters and calibrate the bathymetric prediction results. The experimental results show that the proposed BDNN model provides more reliable and accurate bathymetric prediction results than the conventional DNN and seabed topography inversion models. Moreover, the uncertainty results quantified by the model have a significant spatial correlation with the seabed topography, providing high confidence in the prediction results and reducing the risk in the interpretation of seabed topography, thus proving the potential of BDNN for accurate bathymetric prediction from gravity data.","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":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144704914","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

Dual-Domain Synergistic Pansharpening Network With Region-Adaptive Frequency Convolution 基于区域自适应频率卷积的双域协同泛锐化网络

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-07-15 DOI: 10.1109/LGRS.2025.3589284

Yating Liang;Yi Li;Fan Liu

{"title":"Dual-Domain Synergistic Pansharpening Network With Region-Adaptive Frequency Convolution","authors":"Yating Liang;Yi Li;Fan Liu","doi":"10.1109/LGRS.2025.3589284","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3589284","url":null,"abstract":"Pansharpening is a critical technique in remote sensing aimed at generating high-resolution multispectral (HRMS) images by fusing high-resolution panchromatic (PAN) images with low-resolution multispectral (LRMS) images. However, existing methods face challenges in frequency-domain feature extraction, as global analyses often neglect regional characteristics, while local patch-based approaches may compromise the structural integrity of the image. To address these issues, we propose a novel pansharpening network utilizing a dual-branch architecture to extract frequency-domain features from PAN and MS images. This approach effectively harnesses their complementary information to enhance pansharpening performance. The extracted features are integrated with spatial-domain details via a hierarchical fusion (HF) module, enabling comprehensive reconstruction of HRMS images. In addition, we introduce a novel frequency-domain feature extraction method, termed region-based self-similarity adaptive frequency convolution (RSAFC). This method dynamically adjusts the frequency characteristics of distinct image regions by leveraging cluster-based self-similarity relationships and adaptive convolution operations that combine amplitude and phase, thereby achieving precise modeling of frequency-domain information. Experimental evaluations on the WorldView-3 (WV3) and QuickBird (QB) datasets demonstrate that the proposed method outperforms state-of-the-art approaches in both subjective and objective 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":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144687810","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

CLFDA: Continuous Low-Frequency Decomposition Architecture for Fine-Grained Land Cover Classification CLFDA：用于细粒度土地覆盖分类的连续低频分解架构

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-07-15 DOI: 10.1109/LGRS.2025.3589242

Dongyang Hou;Junwu Xiang;Li Lei;Wenmin Qiu;Mengdi Zhao;Yingjun Luo

{"title":"CLFDA: Continuous Low-Frequency Decomposition Architecture for Fine-Grained Land Cover Classification","authors":"Dongyang Hou;Junwu Xiang;Li Lei;Wenmin Qiu;Mengdi Zhao;Yingjun Luo","doi":"10.1109/LGRS.2025.3589242","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3589242","url":null,"abstract":"Fine-grained land cover classification from high-resolution remote sensing imagery plays a vital role in urban and environmental monitoring. While the existing spatial-domain-based approaches achieve notable progress, their performance in complex scenarios remains constrained by insufficient modeling of characteristics. This letter proposes the continuous low-frequency decomposition architecture (CLFDA) to address insufficient cross-domain modeling of multiscale frequency characteristics in current methods. The architecture introduces frequency-domain features through continuous low-frequency decomposition, where each frequency decomposition and enhancement (FDE) module employ discrete wavelet transform (DWT) to separate spatial and low-frequency features into low-frequency and high-frequency subbands. Low-frequency features feedback into the encoder for global context, while high-frequency features are routed to the decoder via attention mechanisms for detail refinement, enabling bidirectional spatial–frequency fusion. By integrating convolutional neural networks (CNNs), vision transformer, and mamba backbones, our CLFDA achieves 2.0% and 3.46% average mIoU improvements on the GID-15 and the FUSU datasets, respectively. These consistent performance gains across heterogeneous backbones demonstrate the effectiveness and generalizability of our CLFDA in modeling frequency-domain features. The code is at <uri>https://github.com/GeoRSAI/CLFDA</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":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144680872","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

Toward Multitask Perception for Remote Sensing Imagery via Compression and Prompt Tuning 基于压缩和快速调整的遥感图像多任务感知研究

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-07-14 DOI: 10.1109/LGRS.2025.3589030

Yongqiang Wang;Feng Liang;Hang Chen;Haisheng Fu;Jiro Katto

{"title":"Toward Multitask Perception for Remote Sensing Imagery via Compression and Prompt Tuning","authors":"Yongqiang Wang;Feng Liang;Hang Chen;Haisheng Fu;Jiro Katto","doi":"10.1109/LGRS.2025.3589030","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3589030","url":null,"abstract":"Recently, advancements in satellite technology have greatly increased the availability of high-resolution remote sensing images. Concurrently, learning-based image compression (LIC) has significantly improved the efficiency of transmitting and storing such images. As machine recognition tasks increasingly depend on transmitting visual data across devices, compressed images play a key role in both human and machine perception during downstream tasks. However, most LIC approaches are not optimized for machine recognition tasks. To address this limitation, we propose a remote sensing image compression network called RSIC, which integrates multitask perception and supports downstream tasks such as object detection. Specifically, we introduce a wavelet-based frequency-spatial block (WFSB) that separates frequency components and processes them using transformer and convolutional neural network (CNN) blocks to effectively capture frequency-specific features. Within WFSB, the prompting Swin-Transformer block (PSTB) extracts spatial information while enabling prompt tuning. In addition, after primary codec training, instance and task prompts are applied during the encoding and decoding stages, respectively, facilitating machine perception without full fine-tuning. Extensive experimental results show that our model achieves better rate–distortion (R–D) performance for image compression on the aerial image dataset (AID) test dataset, surpassing the traditional versatile video coding (VVC) codec and several recent LIC methods. Furthermore, our method demonstrates superior performance in terms of rate–accuracy for machine perception on the Northwestern Polytechnical University Very-High-Resolution 10-Class Dataset (NWPU VHR-10) and High-Resolution SAR Images Dataset (HRSID) remote sensing datasets.","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":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144687764","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

Low-Frequency Seismic Data Reconstruction Using Deep-Learning Refined Deconvolution 基于深度学习精细反卷积的低频地震数据重建

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-07-14 DOI: 10.1109/LGRS.2025.3588008

Zhaoqi Gao;Weiwei Yang;Qiu Du;Lei Wang;Jinghuai Gao

{"title":"Low-Frequency Seismic Data Reconstruction Using Deep-Learning Refined Deconvolution","authors":"Zhaoqi Gao;Weiwei Yang;Qiu Du;Lei Wang;Jinghuai Gao","doi":"10.1109/LGRS.2025.3588008","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3588008","url":null,"abstract":"Low-frequency (LF) data play a key role in mitigating cycle-skipping in full waveform inversion (FWI). We propose a method to efficiently and accurately reconstruct LF seismic data for a large number of shot gathers based on multichannel deconvolution (MD) and deep learning (DL). Specifically, we first propose an MD method to predict LF data for very limited shot gathers. Then, we use a deep neural network (called “acceleration network”) to learn the relation between a shot gather and its corresponding LF data, based on the labels provided by the MD method, enabling efficient prediction for all shot gathers. Next, another deep neural network (called “improvement network”) is proposed to improve the accuracy of the LF shot gathers predicted by the “acceleration network.” To do so, several horizontal layered velocity models are generated based on the statistical distribution of well logs, and several synthetic shot gathers with and without LF are generated by solving the acoustic wave equation. Based on these synthetic shot gathers, the MD predicted LF data and the corresponding true LF data form a data pair [predicted LF, true LF] for each shot gather, and these data pairs are used to train the “improvement network.” Finally, employing a cascade of “acceleration network” and “improvement network,” we reconstruct the LF data of all shot gathers. Synthetic and field data examples verify that the proposed method exhibits superior accuracy compared to conventional MD method in LF data reconstruction.","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":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144671115","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-Quality MIMO Sonar Imaging Using Cross Correlation Suppression 利用互相关抑制的高品质MIMO声纳成像

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-07-14 DOI: 10.1109/LGRS.2025.3588729

Jiahao Fan;Xionghou Liu;Xin Yao;Yixin Yang

{"title":"High-Quality MIMO Sonar Imaging Using Cross Correlation Suppression","authors":"Jiahao Fan;Xionghou Liu;Xin Yao;Yixin Yang","doi":"10.1109/LGRS.2025.3588729","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3588729","url":null,"abstract":"Multiple-input–multiple-output (MIMO) sonar systems enhance imaging resolution by synthesizing a virtual array produced by orthogonal transmitted waveforms and a bank of matched filters. Up- and down-chirp pulses are widely used due to their approximate orthogonality and robustness for underwater remote sensing applications. However, the cross correlation function (CCF) terms between them always exist and thereby degrade the quality of the MIMO sonar image. In this letter, we propose a novel cross correlation suppression method for improving the image quality of MIMO sonar. The proposed method leverages the frequency-domain differences between the envelopes of autocorrelation functions (ACFs) and CCFs of up- and down-chirp pulses. After the Fourier transformed, the envelopes of ACFs are approximately to be square wave function, while CCFs are approximately to be sinc function. Therefore, CCFs’ energy is concentrated and easier to be suppressed. By setting the main components of the CCFs to zero in the frequency domain of the multibeam outputs’ envelopes, the method effectively suppresses range sidelobes (SLs) caused by cross correlation, thereby enhancing the quality of the imaging results. Through numerical simulations and lake experiments, we quantitatively analyze the performance of the proposed method and demonstrate its effectiveness in MIMO sonar imaging.","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":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144671147","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