IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society最新文献_第5页

BiMAConv: Bimodal Adaptive Convolution for Multispectral Point Cloud Segmentation 基于双峰自适应卷积的多光谱点云分割

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-04-30 DOI: 10.1109/LGRS.2025.3565739

Zheng Zhang;Tingfa Xu;Peng Lou;Peng Lv;Tiehong Tian;Jianan Li

{"title":"BiMAConv: Bimodal Adaptive Convolution for Multispectral Point Cloud Segmentation","authors":"Zheng Zhang;Tingfa Xu;Peng Lou;Peng Lv;Tiehong Tian;Jianan Li","doi":"10.1109/LGRS.2025.3565739","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3565739","url":null,"abstract":"Multispectral point cloud segmentation, leveraging both spatial and spectral information to classify individual points, is crucial for applications such as remote sensing, autonomous driving, and urban planning. However, existing methods primarily focus on spatial information and merge it with spectral data without fully considering their differences, limiting the effective use of spectral information. In this letter, we introduce a novel approach, bimodal adaptive convolution (BiMAConv), which fully exploits information from different modalities, based on the divide-and-conquer philosophy. Specifically, BiMAConv leverages the spectral features provided by the spectral information divergence (SID) and the weight information provided by the modal-weight block (MW-Block) module. The SID highlights slight differences in spectral information, providing detailed differential feature information. The MW-Block module utilizes an attention mechanism to combine generated features with the original point cloud, thereby generating weights to maintain learning balance sharply. In addition, we reconstruct a large-scale urban point cloud dataset GRSS_DFC_2018_3D based on dataset GRSS_DFC_2018 to advance the field of multispectral remote sensing point cloud, with a greater number of categories, more precise annotations, and registered multispectral channels. BiMAConv is fundamentally plug-and-play and supports different shared-multilayer perceptron (MLP) methods with almost no architectural changes. Extensive experiments on GRSS_DFC_2018_3D and Toronto-3D benchmarks demonstrate that our method significantly boosts the performance of popular 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":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143949183","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

EMS-Net: Efficient Multiscale Perceptual Enhancement Tiny Object Detector for Remote Sensing Images

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-04-29 DOI: 10.1109/LGRS.2025.3565583

Pinwei Chen;Wentao Lyu;Qing Guo;Zhijiang Deng;Weiqiang Xu

{"title":"EMS-Net: Efficient Multiscale Perceptual Enhancement Tiny Object Detector for Remote Sensing Images","authors":"Pinwei Chen;Wentao Lyu;Qing Guo;Zhijiang Deng;Weiqiang Xu","doi":"10.1109/LGRS.2025.3565583","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3565583","url":null,"abstract":"Detecting tiny objects in remote sensing images has always been a challenging and intensive research area. This problem has not been well-solved due to the fact that object detection (OD) in remote sensing images is characterized by large-scale variations and complex backgrounds. On this basis, we propose the efficient multi-scale semantic-aware network (EMS-Net) constructed based on YOLOv8s for tiny OD network in remote sensing images. First, a new module multibranch context aggregation (MCA) is proposed to improve deep feature extraction and deep feature fusion of the model. In addition, we use our self-designed multiscale feature communication module (MFCM) aimed at reducing the loss of semantic information of object and mitigating the obstruction of foreground object by complex background. Finally, Wise IoU-Normalized Wasserstein distance (WIoU-NWD) is used as the bounding box regression loss to adapt the model to different object scale while improving the ability to localize tiny object. Comprehensive experiments on three popular datasets demonstrate that our method outperforms existing detectors, particularly in detecting tiny objects. Specifically, our approach achieves the mean average precision (mAP) of 77.2% on the DIOR dataset, 96.7% on the Remote Sensing Object Detection (RSOD) dataset, and 75.1% on the DOTA-v1.5 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":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143943963","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

An Open-Set Domain Adaptation Framework for Hyperspectral Image Classification With Pixel-Aware Weighting and Decoupled Alignment 基于像素感知加权和解耦对齐的高光谱图像分类开集域自适应框架

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-04-29 DOI: 10.1109/LGRS.2025.3565605

Zhaokui Li;Mingtai Qi;Yan Wang;Xuewei Gong;Cuiwei Liu;Jinjun Wang

{"title":"An Open-Set Domain Adaptation Framework for Hyperspectral Image Classification With Pixel-Aware Weighting and Decoupled Alignment","authors":"Zhaokui Li;Mingtai Qi;Yan Wang;Xuewei Gong;Cuiwei Liu;Jinjun Wang","doi":"10.1109/LGRS.2025.3565605","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3565605","url":null,"abstract":"Recent studies have shown that deep domain adaptation (DA) techniques perform excellently in cross-domain hyperspectral image (HSI) classification. However, these methods typically assume that the source domain and the target domain share the same class set, while in practice, the target domain may include unknown classes, and direct alignment can result in negative transfer. Moreover, in HSI classification based on deep learning, using the label of the central pixel to represent the label of the image patch may lead to feature bias due to the uncertainty of the labels of neighboring pixels, thereby reducing the generalization performance of the model. To address this, this letter proposes an open-set DA (OSDA) framework, including a pixel-aware adaptive weight learning (PAWL) module and a decoupled dual alignment (DDA) strategy. The PAWL module effectively reduces the feature bias caused by inconsistency in neighboring pixel labels by analyzing the uncertainty of neighboring pixel labels and using adaptive weight learning, thereby improving recognition performance in open-set environments. The DDA strategy decouples the features of the source domain and target domain into known and unknown classes and aligns them separately to mitigate negative transfer. Experiments on two cross-scene hyperspectral datasets validated the effectiveness of the method. Our source code is available at <uri>https://github.com/Li-ZK/PWDA-2025</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":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144073035","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

Ocean Wave Measurement Using 77-GHz FMCW MIMO Radar at Low Incidence Angles

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-04-29 DOI: 10.1109/LGRS.2025.3565624

Qinghui Xu;Chen Zhao;Fan Ding;Zezong Chen;Sitao Wu;Weibo Chen

引用次数: 0

An Optimized Model With Encoder-Decoder ConvLSTM for Global Ionospheric Forecasting 基于编码器-解码器ConvLSTM的全球电离层预报优化模型

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-04-29 DOI: 10.1109/LGRS.2025.3565645

Cheng Wang;Kaiyu Xue;Chuang Shi

引用次数: 0

Nonlocal Affinity-Based Robust Interference-Resistant Model for Infrared Small Target Detection 基于非局部亲和力的红外小目标鲁棒抗干扰模型

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-04-29 DOI: 10.1109/LGRS.2025.3565538

Jiakun Deng;Xingye Cui;Kexuan Li;Junsong Hu;Chang Long;Yizhuo Yin;Tian Pu;Zhenming Peng

{"title":"Nonlocal Affinity-Based Robust Interference-Resistant Model for Infrared Small Target Detection","authors":"Jiakun Deng;Xingye Cui;Kexuan Li;Junsong Hu;Chang Long;Yizhuo Yin;Tian Pu;Zhenming Peng","doi":"10.1109/LGRS.2025.3565538","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3565538","url":null,"abstract":"Infrared small target detection (ISTD) is a fundamental component of infrared search and tracking (IRST) systems. The low-rank sparse decomposition (LRSD) method has become the mainstream of ISTD due to its broad applicability across various scenarios. However, certain sparse interferences in complex backgrounds may limit the effectiveness of these methods. To solve this problem, we propose a nonlocal affinity-based robust interference-resistant model (NARIRM) for ISTD. The model leverages the concept of affinity, which denotes the relationship between pixel regions, assuming that interference has stronger affinity with its neighbors than the target. The affinity values are achieved by reformulating an infrared image as the linear combination of foreground and background and using sparse decomposition results as constraints. A suppressor is then derived to reduce the impact of sparse interference by the affinity values. Experimental evaluation on public datasets demonstrates the proposed method outperforms several state-of-the-art techniques. The code is available at <uri>https://github.com/djk1997-jk/NARIRM</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":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143925148","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

Re-Evaluation of Lunar Regolith Thickness Using Relative Microwave Brightness Temperature of Chang’E-2 Microwave Radiometer

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-04-28 DOI: 10.1109/LGRS.2025.3564908

Meng Lv;Qianyun Mao;Wenchao Zheng;Guoping Hu

{"title":"Re-Evaluation of Lunar Regolith Thickness Using Relative Microwave Brightness Temperature of Chang’E-2 Microwave Radiometer","authors":"Meng Lv;Qianyun Mao;Wenchao Zheng;Guoping Hu","doi":"10.1109/LGRS.2025.3564908","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3564908","url":null,"abstract":"The exploration of the Moon has never ceased. One of the most significant challenges is determining the thickness of the lunar regolith. This letter employs the relative microwave brightness temperature (TB) to invert the thickness of the lunar regolith. A multilayer parallel stratified model serves as the forward model. In the inversion process, the simulated microwave TB is derived by calculating the sum of the TB contributions from each layer. Based on the forward model, the areas where the simulated TB is sensitive to lunar regolith thickness can be identified. Subsequently, the simulated TB is compared with the measured TB by the Chang’E-2 Microwave Radiometer (MRM) at 3 GHz at midnight (24:00) of the lunar local time. The discrepancy between the observed and modeled TB at a specified location, such as the Apollo 12 landing site (A12), is regarded as a correction for the simulated TBs at other locations with the same latitude. Ultimately, the thickness of the regolith is inverted according to the corrected simulated TB. This letter compares the inverted result with the regolith thickness obtained by DEM data. It is found that regions where the model inverted results closely align with the DEM data tend to have higher FeO/TiO2 content. The uncertainty of the inversion is also discussed, which indicates that the method presented in this letter is feasible.","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":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143943850","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 High-Confidence Homogeneous Features: Partial Neighborhood Ratio Based Difference Image for SAR Change Detection 面向高置信度均匀特征：基于局部邻域比的SAR变化检测差分图像

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-04-28 DOI: 10.1109/LGRS.2025.3564600

Bin Cui;Yao Peng;Huarong Jia;Shanchuan Guo;Peijun Du

{"title":"Toward High-Confidence Homogeneous Features: Partial Neighborhood Ratio Based Difference Image for SAR Change Detection","authors":"Bin Cui;Yao Peng;Huarong Jia;Shanchuan Guo;Peijun Du","doi":"10.1109/LGRS.2025.3564600","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3564600","url":null,"abstract":"The inherent speckle noise in synthetic aperture radar (SAR) images limits the accuracy of SAR image change detection. As a crucial step in unsupervised change detection, existing difference map generation methods primarily utilize neighborhood information to counteract the interference caused by speckle noise. However, pixels within the neighborhood can themselves be affected by heterogeneous pixels and noise. Therefore, this letter proposes a difference map generation method, partial neighborhood ratio (PNR), which relies on high-confidence homogeneous pixels within the neighborhood for difference calculation. Specifically, under the assumption that the local neighborhood of SAR images follows a normal distribution, we develop a method for selecting high-confidence homogeneous pixels. This method quantifies interneighborhood dissimilarity by leveraging the statistical features of predominantly homogeneous pixel clusters within an adaptive framework, thereby reducing the impact of noise and enhancing the accuracy of difference expression. Experimental results demonstrate the superior performance of the proposed PNR. The change detection results, obtained by applying both manual trial-and-error and dual-domain network (DDNet) on three SAR datasets, have validated the effectiveness of the proposed algorithm.","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":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143949258","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-Branch Retrieval Network for Satellite Cloud Image Classification Based on Multilevel Semantic Information 基于多级语义信息的卫星云图分类双分支检索网络

IEEE geoscience and remote sensing letters : a publication of the IEEE Geoscience and Remote Sensing Society Pub Date : 2025-04-28 DOI: 10.1109/LGRS.2025.3564728

Jiezhi Lv;Nan Wu;Wei Jin;Randi Fu

{"title":"Dual-Branch Retrieval Network for Satellite Cloud Image Classification Based on Multilevel Semantic Information","authors":"Jiezhi Lv;Nan Wu;Wei Jin;Randi Fu","doi":"10.1109/LGRS.2025.3564728","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3564728","url":null,"abstract":"The weather system has a profound impact on human activities. Conducting research on satellite cloud image classification can provide critical parameters for weather forecasting, climate analysis, and severe weather detection. However, conventional satellite cloud image classification methods typically neglect higher level semantic constraints and rarely incorporate decision-level adaptive calibration, resulting in confusion among visually similar categories and restricting interpretable, content-based inference. Here, we propose a dual-branch retrieval network with multilevel semantic information (DBR-MSI) to address these gaps. DBR-MSI jointly optimizes high-level semantics (e.g., broad meteorological and surface categories) and low-level semantics (e.g., specific cloud or surface attributes), and we explicitly highlight critical semantic content via a gradient-based attention sharing module. Moreover, a retrieval-based inference approach driven by high-level semantic guidance supports interpretable content reasoning and adaptive decision calibration, which in turn allows the proposed method to deliver enhanced robustness and efficient integration of additional data. Experimental results on two satellite cloud image datasets confirm that DBR-MSI exhibits stronger interpretability and achieves overall accuracy (OA) gains of 1.06% and 0.39% over the best competing methods.","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":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143931316","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

Enhancing Crop Yield Estimation Through Iterative Querying and Bayesian-Optimized Gated Networks 利用迭代查询和贝叶斯优化门控网络提高作物产量估计

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.2025.3564415

Benjamin K. Osibo;Tinghuai Ma;Kristina Darbinian;Bright Bediako-Kyeremeh;Lorenzo Mamelona;Jianxin Liu;Stephen Osei-Appiah

{"title":"Enhancing Crop Yield Estimation Through Iterative Querying and Bayesian-Optimized Gated Networks","authors":"Benjamin K. Osibo;Tinghuai Ma;Kristina Darbinian;Bright Bediako-Kyeremeh;Lorenzo Mamelona;Jianxin Liu;Stephen Osei-Appiah","doi":"10.1109/LGRS.2025.3564415","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3564415","url":null,"abstract":"The accurate prediction of crop yield is essential not only for sustainable agriculture but also for ensuring global food security. In recent times, deep learning (DL) techniques have made significant strides in improving prediction accuracy by leveraging complex and advanced architectures. However, despite these advancements, the existing methods often struggle in modeling temporal dependencies efficiently, especially when dealing with limited data (a common challenge in crop yield prediction). To address this, an innovative iterative querying (IQ) strategy based on the principles of active learning (AL) to enhance model performance has been proposed. The aim of the IQ strategy is to maximize performance by introducing the model to a batch of uncertain instances in each iteration. The overall prediction framework consists of two key components: first, a Bayesian-optimized gated recurrent unit (GRU) method to capture the complex temporal relationships between crop variables and target yield; and second, the novel IQ strategy, which utilizes an uncertainty-driven query mechanism to refine predictions by focusing on the most challenging and uncertain data points. A comprehensive multisource data, comprising remotely sensed variables, climatic, soil, and corresponding crop yield values from the US Corn Belt region, are used to train and evaluate the proposed IQ-GRU method. Experimental results demonstrate the effectiveness of the proposed IQ-GRU framework in improving yield estimation for both in-season and end-of-season predictions over conventional methods.","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":"143949259","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