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

A Deep-Learning-Based Targeted Interpolation Method for Seismic Data: A Consecutively Missing Trace VSP Case 基于深度学习的地震数据目标插值方法：连续缺失道VSP案例

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.2025.3565742

Wen Yang;Qianggong Song;Le Li;Xiaobin Li;Zhonglin Cao;Pengfei Duan

{"title":"A Deep-Learning-Based Targeted Interpolation Method for Seismic Data: A Consecutively Missing Trace VSP Case","authors":"Wen Yang;Qianggong Song;Le Li;Xiaobin Li;Zhonglin Cao;Pengfei Duan","doi":"10.1109/LGRS.2025.3565742","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3565742","url":null,"abstract":"Seismic data interpolation is an important processing method for improving the quality of seismic data. Traditional interpolation methods often face limitations due to their dependence on prior information and their challenges in processing continuous missing data. Vertical seismic profiling (VSP) data, owing to its unique acquisition approach, generally do not suffer from missing receivers but can have missing shots, with the locations of these missing shots being known. To address this specific issue of missing shots, a specialized interpolation technique has been proposed for targeted missing data. This technique involves creating datasets from the original complete data that are tailored to fixed missing shot scenarios, allowing for a more effective application of the trained network to field data. In addition, we have optimized the network structure based on UNet to meet the specific requirements for handling consecutive gaps. Both synthetic and field data demonstrate the effectiveness of this targeted interpolation method.","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":"144072855","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

Multi-Grained Guided Diffusion for Quantity-Controlled Remote Sensing Object Generation 多粒度制导扩散用于量控遥感目标生成

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.2025.3565817

Zhiping Yu;Chenyang Liu;Chuyu Zhong;Zhengxia Zou;Zhenwei Shi

{"title":"Multi-Grained Guided Diffusion for Quantity-Controlled Remote Sensing Object Generation","authors":"Zhiping Yu;Chenyang Liu;Chuyu Zhong;Zhengxia Zou;Zhenwei Shi","doi":"10.1109/LGRS.2025.3565817","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3565817","url":null,"abstract":"Accurate object counts represent essential semantical information in remote sensing imagery, significantly impacting applications such as traffic monitoring and urban planning. Despite the recent advances in text-to-image (T2I) generation in remote sensing, existing methods still face challenges in precisely controlling the number of object instances in generated images. To address this challenge, we propose a novel method, multi-grained guided diffusion (MGDiff). During training, unlike previous methods that relied solely on latent-space noise constraints, MGDiff imposes constraints at three distinct granularities: latent pixel, global counting, and spatial distribution. The multi-grained guidance mechanism matches the quantity prompts with object spatial layouts in the feature space, enabling our model to achieve precise control over object quantities. To benchmark this new task, we present Levir-QCG, a dataset comprising 10504 remote sensing images across five object categories, annotated with precise object counts and segmentation masks. We conducted extensive experiments to benchmark our method against previous methods on the Levir-QCG dataset. Compared to previous models, the MGDiff achieves an approximately +40% improvement in counting accuracy while maintaining higher visual fidelity and strong zero-shot generalization. To the best of our knowledge, this is the first work to research accurate object quantity control in remote sensing T2I generation. The dataset and code will be publicly available at <uri>https://github.com/YZPioneer/MGDiff</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":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143949224","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

Establishing Nuanced Multimodal Attention for Weakly Supervised Semantic Segmentation of Remote Sensing Scenes

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.2025.3565710

Qiming Zhang;Junjie Zhang;Huaxi Huang;Fangyu Wu;Hongwen Yu

{"title":"Establishing Nuanced Multimodal Attention for Weakly Supervised Semantic Segmentation of Remote Sensing Scenes","authors":"Qiming Zhang;Junjie Zhang;Huaxi Huang;Fangyu Wu;Hongwen Yu","doi":"10.1109/LGRS.2025.3565710","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3565710","url":null,"abstract":"Weakly supervised semantic segmentation (WSSS) with image-level labels reduces reliance on pixel-level annotations for remote sensing (RS) imagery. However, in natural scenes, WSSS frequently faces challenges such as imprecise localization, extraneous activations, and class ambiguity. These challenges are particularly pronounced in RS images, characterized by complex backgrounds, substantial scale variations, and dense small-object distributions, complicating the distinction between intraclass variations and interclass similarities. To tackle these challenges, we introduce a class-constrained multimodal attention framework aimed at enhancing the localization accuracy of class activation maps (CAMs). Specifically, we design class-specific tokens to capture the visual characteristics of each target class. As these tokens initially lack explicit constraints, we integrate the textual branch of the RemoteCLIP model to leverage class-related linguistic priors, which collaborate with visual features to encode the specific semantics of diverse objects. Furthermore, the multimodal collaborative optimization module dynamically establishes tailored attention mechanisms for both global and regional features, thereby improving class discriminability among targets to mitigate challenges such as interclass similarity and dense small-object distributions. By refining class-specific attention, textual semantic attention, and patch-level pairwise affinity weights, the quality of generated pseudomasks is markedly enhanced. Concurrently, to ensure domain-invariant feature learning, we align the backbone features with the CLIP visual embedding by minimizing the distribution disparity between the two in the latent space, and semantic consistency is, therefore, preserved. The experimental results validate the effectiveness and robustness of our proposed method, achieving significant performance improvements on two representative RS WSSS 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":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143943919","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 Advanced Approach for Understory Terrain Extraction Utilizing TomoSAR and MCSF Algorithm 基于TomoSAR和MCSF算法的林下地形提取方法

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.2025.3565785

Xi Bin;Zhang Yu;Li Wenmei;Zhao Lei;Xu Kunpeng;Ma Yunmei;He Yuhong

{"title":"An Advanced Approach for Understory Terrain Extraction Utilizing TomoSAR and MCSF Algorithm","authors":"Xi Bin;Zhang Yu;Li Wenmei;Zhao Lei;Xu Kunpeng;Ma Yunmei;He Yuhong","doi":"10.1109/LGRS.2025.3565785","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3565785","url":null,"abstract":"The understory terrain is an essential component of forest vertical structure and ecosystem health, providing crucial insights for resource assessment and forestry surveys. This letter proposes a novel method for extracting understory terrain through forest backscattering power profiles and the modified cloth simulation filtering (MCSF) algorithm. It innovatively reconstructs synthetic aperture radar (SAR) signals into a 3-D point cloud, eliminating sidelobe signals to reduce noise while only retaining the mainlobe signals. The MCSF algorithm is subsequently utilized to extract ground and nonground points based on the vertical distribution of the mainlobe signals. The extracted ground points offer a more precise representation of actual terrain conditions. The feasibility of the method was validated utilizing airborne P-band multi baseline SAR data obtained from the Saihanba test site in Hebei Province. The outcomes clearly indicate that our approach exhibits superior correlation (0.999) and a smaller root mean square error (RMSE) (3.07 m) in comparison to conventional methods when compared with the reference digital elevation model (DEM).","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":"143938007","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

Covariance Matrix Estimation via Geometric Median in Highly Heterogeneous PolSAR Images 基于几何中值的高度非均匀偏振sar图像协方差矩阵估计

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.2025.3565808

Dehbia Hanis;Luca Pallotta;Karima Hadj-Rabah;Azzedine Bouaraba;Aichouche Belhadj-Aissa

{"title":"Covariance Matrix Estimation via Geometric Median in Highly Heterogeneous PolSAR Images","authors":"Dehbia Hanis;Luca Pallotta;Karima Hadj-Rabah;Azzedine Bouaraba;Aichouche Belhadj-Aissa","doi":"10.1109/LGRS.2025.3565808","DOIUrl":"https://doi.org/10.1109/LGRS.2025.3565808","url":null,"abstract":"The Wishart distribution is a well-established statistical model for characterizing the density of random variables in polarimetric synthetic aperture radar (PolSAR) data, particularly within homogeneous regions where Gaussian assumptions hold. However, as PolSAR applications expand into heterogeneous environments, alternative statistical models have been developed to better capture the complexity of such areas, playing an important role in tasks such as classification. In this study, we examine the effectiveness of covariance matrix estimation using the median matrix, a technique grounded in optimal transport theory and validated in prior research for its effectiveness. Building on this foundation, we propose the application of a statistical model tailored for heterogeneous regions, i.e., following the <inline-formula> <tex-math>$mathcal {G}^{0}_{P}$ </tex-math></inline-formula> distribution, addressing the limitations of traditional assumptions. This method is particularly suitable for high-resolution PolSAR datasets, where the homogeneity hypothesis often does not hold. The experimental results obtained using L-band PolSAR images acquired over Foulum in Denmark demonstrate the robustness of our proposed variant.","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":"144073101","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

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