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

Dynamic Spectral Similarity Method (DSSM)—A Novel Method for Automated Identification of Objects in Hyperspectral Imagery 动态光谱相似度法（DSSM）——一种新的高光谱图像目标自动识别方法

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-04-25 DOI: 10.1109/LGRS.2025.3564386

Harsha Chandra;Rama Rao Nidamanuri

{"title":"Dynamic Spectral Similarity Method (DSSM)—A Novel Method for Automated Identification of Objects in Hyperspectral Imagery","authors":"Harsha Chandra;Rama Rao Nidamanuri","doi":"10.1109/LGRS.2025.3564386","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3564386","url":null,"abstract":"Automatic identification of object of interest in a hyperspectral imagery is promising for remote sensing applications. Spectral knowledge transfer enables autonomous comparison of reference and imagery spectra for expert-independent analysis. Knowledge-transfer-based analysis involves comparing image spectra to the reference spectra (spectral libraries) using spectral similarity metrics. However, the reference spectral databases and the imagery acquired by different sensors differ in spectral resolution and bandwidths, limiting the direct comparison of the spectra. Thus, prerequisite process of spectral resampling is required before the analysis. We propose a new method “dynamic spectral similarity method (DSSM)” that quantitatively compares spectra from sensors having different spectral resolutions. DSSM geometrically aligns two nonlinear spectra and computes an optimal alignment cost through a time-warping process in a dynamic feature space. We demonstrated the potential of DSSM by comparing spectra of diverse landscape elements obtained from various sources (satellites, airborne, spectral libraries) against reference databases. Furthermore, the proposed method is compared with spectral matching methods [spectral angle mapper (SAM), spectral information divergence2 (SID), normalized spectral similarity score (NS3)] after a spectral alignment process using a Gaussian diffusion model. The results are promising, offering 80%–90% matching accuracy in all the scenarios. DSSM enables seamless comparison of images with varying spectral characteristics, allowing selective and automatic object identification.","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":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144073037","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

Dropout Concrete Autoencoder for Band Selection on Hyperspectral Image Scenes 用于高光谱图像场景波段选择的Dropout混凝土自编码器

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-04-25 DOI: 10.1109/LGRS.2025.3564478

Lei Xu;Mete Ahishali;Moncef Gabbouj

{"title":"Dropout Concrete Autoencoder for Band Selection on Hyperspectral Image Scenes","authors":"Lei Xu;Mete Ahishali;Moncef Gabbouj","doi":"10.1109/LGRS.2025.3564478","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3564478","url":null,"abstract":"Deep learning-based informative band selection methods on hyperspectral images (HSIs) have recently gained intense attention to eliminate spectral correlation and redundancies. However, existing deep learning-based methods either need additional postprocessing strategies to select the descriptive bands or optimize the model indirectly due to the parameterization inability of discrete variables for the selection procedure. To overcome these limitations, this work proposes a novel end-to-end network for informative band selection. The proposed network, named Dropout concrete autoencoder (CAE), is inspired by advances in the CAE and Dropout feature ranking (Dropout FR) strategy. Unlike traditional deep learning-based methods; the Dropout CAE is trained directly given the required band subset, eliminating the need for further postprocessing. The experimental results in four HSI scenes show that the Dropout CAE achieves substantial and effective performance levels that outperform competing methods. The code is available at <uri>https://github.com/LeiXuAI/Hyperspectral</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":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10976710","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144073097","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

YOLO-G3CF: Gaussian Contrastive Cross-Channel Fusion for Multimodal Object Detection YOLO-G3CF：用于多模态目标检测的高斯对比跨通道融合

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-04-24 DOI: 10.1109/LGRS.2025.3564181

Abdelbadie Belmouhcine;Minh-Tan Pham;Sébastien Lefèvre

{"title":"YOLO-G3CF: Gaussian Contrastive Cross-Channel Fusion for Multimodal Object Detection","authors":"Abdelbadie Belmouhcine;Minh-Tan Pham;Sébastien Lefèvre","doi":"10.1109/LGRS.2025.3564181","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3564181","url":null,"abstract":"Object detection is a crucial task in both computer vision and remote sensing. The performance of object detectors can vary across different modalities depending on lighting and weather conditions. To address these challenges, we propose a fusion module based on contrastive learning and Gaussian cross-channel attention, called Gaussian contrastive cross-channel fusion (G3CF). We integrate this module into a dual-you only look once (YOLO) architecture, forming YOLO-G3CF. The contrastive loss enforces similarity between the features sent to the detection head from both modality branches, as they should lead to the same detections. The Gaussian attention mechanism enables the model to fuse features in a higher dimensional space, enhancing discriminative power. Extensive experiments on VEDAI, GeoImageNet, VTUAV-det, and FLIR demonstrate that G3CF improves detection performance, achieving a mAP increase of up to 6.64% over the best single-modality baselines and outperforming prior multimodal fusion methods. Regarding model complexity, our fusion method operates at a late stage, increasing the computational cost of single-modality YOLO by approximately 150% in terms of giga floating-point operations per second (GFLOP). For instance, YOLOv8 requires 52.84 GFLOPs, whereas YOLOv8-G3CF, due to its dual architecture and three G3CF modules, increases this to 131.22 GFLOPs. However, a single G3CF module requires only ~15 GFLOPs. Despite this overhead, our approach remains computationally less expensive than transformer-based models, e.g., ICAFusion requires 284.80 GFLOPs. Moreover, the proposed method still operates in real-time, achieving ~19 FPS on an NVIDIA RTX 2080. The code is available at <uri>https://github.com/abelmouhcine/YOLO-G3CF</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":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143913521","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

BTCDNet: Bayesian Tile Attention Network for Hyperspectral Image Change Detection BTCDNet：用于高光谱图像变化检测的贝叶斯块关注网络

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-04-24 DOI: 10.1109/LGRS.2025.3563897

Junshen Luo;Jiahe Li;Xinlin Chu;Sai Yang;Lingjun Tao;Qian Shi

引用次数: 0

LH-UNet: A Lighted Histoformer-Encoded U-Net for Sea Ice Recognition With High-Resolution Remote Sensing Images h- unet：用于高分辨率遥感图像海冰识别的light Histoformer-Encoded U-Net

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-04-23 DOI: 10.1109/LGRS.2025.3563727

Zuomin Wang;Ying Li;Jiazhu Wang;Bingxin Liu

{"title":"LH-UNet: A Lighted Histoformer-Encoded U-Net for Sea Ice Recognition With High-Resolution Remote Sensing Images","authors":"Zuomin Wang;Ying Li;Jiazhu Wang;Bingxin Liu","doi":"10.1109/LGRS.2025.3563727","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3563727","url":null,"abstract":"The background information is complex for sea ice monitoring when using high-resolution remote sensing images, which may lead to a certain degree of difficulty in extracting sea ice information. Lighted histoformer-encoded u-shaped convolutional network (LH-Unet), a semantic segmentation neural network for sea ice fine recognition is proposed in this study. Initially, a histogram transformer block (HTB) in the histoformer model was integrated into the encoder to enhance the accuracy of sea ice recognition. Additionally, a ghost convolution enhanced by a triple attention block (GTB) was introduced, significantly reducing the number of parameters and computational load while also improving accuracy. Furthermore, the mean intersection over union (mIoU) of the LH-UNet network proposed in this study was 96.25%, and the number of parameters of the mentioned architecture is less than 1 M. Notably, LH-UNet surpasses the performance of prominent deep learning methods, including U-Net, PSPNet, HRNet, and SegFormer. The results suggest a reliable technical support for sea ice identification basing on high-resolution remote sensing. Moreover, this study provides a possibility for the monitoring and early warning of sea ice distribution.","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":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143925146","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

Spatiotemporal Attention Network for Chl-a Prediction With Sparse Multifactor Observations 稀疏多因子观测Chl-a预测的时空注意网络

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-04-23 DOI: 10.1109/LGRS.2025.3563458

Xudong Jiang;Yunfan Liu;Shuyu Wang;Wengen Li;Jihong Guan

{"title":"Spatiotemporal Attention Network for Chl-a Prediction With Sparse Multifactor Observations","authors":"Xudong Jiang;Yunfan Liu;Shuyu Wang;Wengen Li;Jihong Guan","doi":"10.1109/LGRS.2025.3563458","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3563458","url":null,"abstract":"Chlorophyll-a (Chl-a) is a critical indicator of water quality, and accurate Chl-a prediction is essential for marine ecosystem protection. However, existing methods for Chl-a prediction cannot adequately uncover the correlations between Chl-a and other environmental factors, e.g., sea surface temperature (SST) and photosynthetically active radiation (PAR). In addition, it is also difficult for these methods to learn the burst distributions of Chl-a data, i.e., increasing sharply for certain short periods of time and remaining stable for the rest of time. Furthermore, as original Chl-a, SST, and PAR data are often of high sparsity, most approaches rely on complete reanalysis data, which can incur accumulated error accumulation and degrade prediction performance. To address these three issues, we proposed a spatiotemporal attention network entitled SMO-STANet for Chl-a prediction. Concretely, the multibranch spatiotemporal embedding module and spatiotemporal attention module are developed to learn the correlations between Chl-a and the two external factors, i.e., SST and PAR, thus facilitating the learning of the underlying spatiotemporal distribution of Chl-a. In addition, we designed a scaled loss function to enable SMO-STANet to adapt to the burst distributions of Chl-a. Finally, we develop a sparse observation data completion module to address the issue of data sparsity. According to the experimental results on two real datasets, SMO-STANet outperforms existing methods for Chl-a prediction by a large margin. The code is available at <uri>https://github.com/ADMIS-TONGJI/SMO-STANet</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":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144073017","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

Single-Frame Infrared Small Target Detection With Dynamic Multidimensional Convolution 基于动态多维卷积的单帧红外小目标检测

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-04-23 DOI: 10.1109/LGRS.2025.3563588

Shichao Zhou;Zekai Zhang;Yingrui Zhao;Wenzheng Wang;Zhuowei Wang

{"title":"Single-Frame Infrared Small Target Detection With Dynamic Multidimensional Convolution","authors":"Shichao Zhou;Zekai Zhang;Yingrui Zhao;Wenzheng Wang;Zhuowei Wang","doi":"10.1109/LGRS.2025.3563588","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3563588","url":null,"abstract":"Mainly resulting from remote imaging, the target of interest in infrared imagery tends to occupy very few pixels with faint radiation value. The absence of discriminative spatial features of infrared small targets challenges traditional single-frame detectors that rely on handcrafted filter engineering to amplify local contrast. Recently, emerging deep convolutional network (DCN)-based detectors use elaborate multiscale spatial contexts representation to “semantically reason” the small and dim infrared target in pixel level. However, the multiple spatial convolution-downsampling operation adopted by such leading methods could cause the loss of target appearance information during the initial feature encoding stage. To further enhance the low-level feature representation capacity, we advocate the insight of traditional matching filter and propose a novel pixel-adaptive convolution kernel modulated by multidimensional contexts (i.e., dynamic multidimensional convolution, DMConv). Precisely, the DMConv is refined by three collaborative and indispensable attention functions that focus on spatial layout, channel, and kernel number of convolution kernel, respectively, so as to effectively mine, highlight, and enrich fine-grained spatial features with moderate computational burden. Extensive experiments conducted on two real-world infrared single-frame image datasets, i.e., SIRST and Infrared Small Target Detection (IRSTD)-1k, favorably demonstrate the effectiveness of the proposed method and obtain consistent performance improvements over other state-of-the-art (SOTA) detectors.","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":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143918653","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

SAIG: Semantic-Aware ISAR Generation via Component-Level Semantic Segmentation 基于组件级语义分割的语义感知ISAR生成

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-04-23 DOI: 10.1109/LGRS.2025.3563712

Yuxin Zhao;Huaizhang Liao;Derong Kong;Zhixiong Yang;Jingyuan Xia

引用次数: 0

Spatial–Frequency Multiple Feature Alignment for Cross-Domain Remote Sensing Scene Classification 面向跨域遥感场景分类的空频多特征对准

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-04-22 DOI: 10.1109/LGRS.2025.3563349

Dongyang Hou;Yang Yang;Siyuan Wang;Xiaoguang Zhou;Wei Wang

{"title":"Spatial–Frequency Multiple Feature Alignment for Cross-Domain Remote Sensing Scene Classification","authors":"Dongyang Hou;Yang Yang;Siyuan Wang;Xiaoguang Zhou;Wei Wang","doi":"10.1109/LGRS.2025.3563349","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3563349","url":null,"abstract":"Domain adaptation is a pivotal technique for improving the classification performance of remote sensing scenes impacted by data distribution shifts. The existing spatial-domain feature alignment methods are vulnerable to complex scene clutter and spectral variations. Considering the robustness of frequency representation in preserving edge details and structural patterns, this letter presents a novel spatial-frequency multiple alignment domain adaptation (SFMDA) method for remote sensing scene classification. First, a frequency-domain invariant feature learning module is introduced, which employs the Fourier transform and high-frequency mask strategy to derive frequency-domain features exhibiting enhanced interdomain invariance. Subsequently, a spatial-frequency feature cross fusion module is developed to achieve more robust and domain-representative spatial-frequency fusion representations through dot product attention and interaction mechanisms. Finally, a multiple feature alignment strategy is devised to minimize both spatial-domain feature differences and fusion feature discrepancies across the source and target domains, thereby facilitating more effective interdomain knowledge transfer. Experimental results on six cross-domain scenarios demonstrate that SFMDA outperforms eight state-of-the-art (SOTA) methods, achieving a 3.87%–17.98% accuracy improvement. Furthermore, SFMDA is compatible with the existing spatial-domain learning frameworks, enabling seamless integration for further performance gains. Our code will be available at <uri>https://github.com/GeoRSAI/SFMDA</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":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143892450","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

QS-FDTD Modeling of Dispersive Superparamagnetic Soils for Time-Domain Electromagnetic Method 色散超顺磁性土的时域电磁法QS-FDTD建模

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-04-22 DOI: 10.1109/LGRS.2025.3561498

Talha Saydam;Serkan Aksoy

{"title":"QS-FDTD Modeling of Dispersive Superparamagnetic Soils for Time-Domain Electromagnetic Method","authors":"Talha Saydam;Serkan Aksoy","doi":"10.1109/LGRS.2025.3561498","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3561498","url":null,"abstract":"Time-domain electromagnetic (TDEM) method is extensively utilized in geophysical surveys for detection of groundwater and mineral deposits. However, the dispersive effect of superparamagnetic (SPM) soils significantly impacts performance of these systems. In modeling of the SPM soils, the distribution of magnetic particles in the soil is generally accounted with a log-uniform model in which time-relaxation constants are distributed uniformly in a finite time range. In this study, the effect of the SPM soils on the TDEM system performance is analyzed by a quasi-static finite-difference time-domain (QS-FDTD) method. The treatment of the magnetic dispersive SPM soil of the log-uniform model is performed by an auxiliary differential equation (ADE) technique (without any convolution) in the QS-FDTD method. The numerical results are validated for a homogeneous SPM half-space problem and a problem of a thin SPM upper layer with a buried conductive body. Afterward, a dispersive complex problem is also solved. The obtained results can be used to evaluate the TDEM performance for the complex magnetic dispersive problems.","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":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143896502","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