IEEE Transactions on Geoscience and Remote Sensing最新文献_第9页

High-Resolution Seamless Mapping of the Leaf Area Index via Multisource Data and the Transformer Deep Learning Model 基于多源数据和Transformer深度学习模型的叶面积指数高分辨率无缝映射

IF 8.2 1区地球科学

IEEE Transactions on Geoscience and Remote Sensing Pub Date : 2025-04-16 DOI: 10.1109/tgrs.2025.3561326

Pengfei Chen, Ke Zhou, Hongliang Fang

引用次数: 0

Generative Shadow Synthesis and Removal for Remote Sensing Images Through Embedding Illumination Models 基于嵌入光照模型的遥感图像生成阴影合成与去除

IF 7.5 1区地球科学

IEEE Transactions on Geoscience and Remote Sensing Pub Date : 2025-04-16 DOI: 10.1109/TGRS.2025.3561307

Chenglin Shao;Huifang Li;Huanfeng Shen

{"title":"Generative Shadow Synthesis and Removal for Remote Sensing Images Through Embedding Illumination Models","authors":"Chenglin Shao;Huifang Li;Huanfeng Shen","doi":"10.1109/TGRS.2025.3561307","DOIUrl":"10.1109/TGRS.2025.3561307","url":null,"abstract":"Shadows significantly reduce the available information in remote sensing images, obstructing downstream tasks such as object detection, scene classification, and localization. Shadow removal from remote sensing images is, however, still an open issue for the following reasons. First, deep neural networks are difficult to train since the corresponding ground truth of shadows is almost always unavailable in practice. Second, the existing shadow removal methods still suffer from blurry details and boundary artifacts. In this article, we describe how a generative shadow synthesis and removal framework that couples data-driven methods with illumination models was developed to address the above challenges effectively. Various shadows were synthesized in shadow-free regions of remote sensing images by GSS-Net, which is a generative shadow synthesis network that considers the physical process of shadow illumination attenuation (SIA). In this way, a large-scale, diverse, and realistic shadow dataset (RS-SynShadow) was built. A generative shadow removal network—GSR-Net—embedding a histogram-enhanced illumination (HEI) model was then developed for high-fidelity shadow removal without artifacts. Extensive experiments conducted on synthetic and real data demonstrate that the proposed shadow synthesis and removal framework significantly outperforms the state-of-the-art methods, both visually and quantitatively. The dataset and code will be made available at <uri>https://github.com/fzzfRS/RS-GSSR</uri>","PeriodicalId":13213,"journal":{"name":"IEEE Transactions on Geoscience and Remote Sensing","volume":"63 ","pages":"1-15"},"PeriodicalIF":7.5,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143841887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dynamic Semantics-Guided Meta-Transfer Learning for Few-Shot SAR Target Detection 基于动态语义引导的元迁移学习的少弹SAR目标检测

IF 8.2 1区地球科学

IEEE Transactions on Geoscience and Remote Sensing Pub Date : 2025-04-16 DOI: 10.1109/tgrs.2025.3561682

Zheng Zhou, Zongyong Cui, Yu Tian, Yongjia Chen, Yiming Pi, Zongjie Cao

引用次数: 0

MASS-Net: Multi-aspect SAR Stereo Network for Target 3-D Reconstruction MASS-Net：面向目标三维重建的多面向SAR立体网络

IF 8.2 1区地球科学

IEEE Transactions on Geoscience and Remote Sensing Pub Date : 2025-04-16 DOI: 10.1109/tgrs.2025.3561466

Jiawei Huo, Zhongyu Li, Hongyang An, Yue Song, Junjie Wu, Jianyu Yang

引用次数: 0

Complex-valued SAR Image Super-Resolution via Sub-aperture Learning and Fusion 基于子孔径学习与融合的复值SAR图像超分辨率研究

IF 8.2 1区地球科学

IEEE Transactions on Geoscience and Remote Sensing Pub Date : 2025-04-16 DOI: 10.1109/tgrs.2025.3561301

Ganggang Dong, Yao Wang, Hongwei Liu, Songlin Liu

引用次数: 0

Depth Feature Extraction for Hyperspectral Image Small Sample Classification 基于深度特征提取的高光谱图像小样本分类

IF 7.5 1区地球科学

IEEE Transactions on Geoscience and Remote Sensing Pub Date : 2025-04-16 DOI: 10.1109/TGRS.2025.3558817

Bing Liu;Xiaohui Chen;Zhixiang Xue;Pengqiang Zhang;Bing Zhang;Jiaying Yue

{"title":"Depth Feature Extraction for Hyperspectral Image Small Sample Classification","authors":"Bing Liu;Xiaohui Chen;Zhixiang Xue;Pengqiang Zhang;Bing Zhang;Jiaying Yue","doi":"10.1109/TGRS.2025.3558817","DOIUrl":"10.1109/TGRS.2025.3558817","url":null,"abstract":"The problem of insufficient labeled samples has restricted the application of deep learning method in hyperspectral image (HSI) classification tasks. Fusion of remote sensing images from different sources such as HSI and LiDAR is a common strategy to improve the classification accuracy. However, obtaining multisource registered remote sensing images of the same area is time-consuming, which limits the application of multisource strategy in practice. Motivated by the recent success of large models in different fields, we propose to extract depth information from large models and fuse it with HSIs to improve the small sample classification accuracy. Specifically, we use the pretrained foundation large model to estimate the depth information of HSIs as the depth features, and then input the original spectral features and depth features into the support vector machine (SVM) to complete the classification. In order to further improve the classification accuracy, we propose to use the sliding window method to extract the depth features of different bands, so as to obtain more rich depth features. A large number of classification experiments on six benchmark datasets verify the effectiveness of the proposed method.","PeriodicalId":13213,"journal":{"name":"IEEE Transactions on Geoscience and Remote Sensing","volume":"63 ","pages":"1-12"},"PeriodicalIF":7.5,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143841890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Unsupervised Remote Sensing Image Semantic Segmentation Based on Multi-Scale Contrastive Domain Adaptation 基于多尺度对比域自适应的无监督遥感图像语义分割

IF 8.2 1区地球科学

IEEE Transactions on Geoscience and Remote Sensing Pub Date : 2025-04-15 DOI: 10.1109/tgrs.2025.3560673

Jie Geng, Shuai Song, Zhe Xu, Wen Jiang

引用次数: 0

Gravity and Magnetic Data Extraction Based on Multispatial Sparsity Optimization 基于多空间稀疏度优化的重磁数据提取

IF 7.5 1区地球科学

IEEE Transactions on Geoscience and Remote Sensing Pub Date : 2025-04-15 DOI: 10.1109/TGRS.2025.3560979

Dan Zhu;Xiangyun Hu;Shuang Liu;Danping Cao

{"title":"Gravity and Magnetic Data Extraction Based on Multispatial Sparsity Optimization","authors":"Dan Zhu;Xiangyun Hu;Shuang Liu;Danping Cao","doi":"10.1109/TGRS.2025.3560979","DOIUrl":"10.1109/TGRS.2025.3560979","url":null,"abstract":"Gravity and magnetic anomalies contain abundant geological information. However, redundant information complicates the study of exploration targets. Existing methods primarily rely on exploiting spectral differences between shallow and deep sources to separate anomalies of different depths. Nevertheless, spectral overlap limits these conventional methods to separating anomalies caused by significantly different depth sources. To reduce effects due to spectral overlap, we propose a novel method for potential field separation. This method capitalizes on the sparsity of gravity and magnetic data in both singular spectrum and model spaces and employs a single-layer equivalent source to represent anomalies induced by target sources. The anomalies caused by sources with different depths can be separated. After sparsely approximating single-layer equivalent sources, we obtain the local anomalies caused by sources within the same layer. Synthetic model experiments demonstrate that the proposed method achieves high separation accuracy, particularly with respect to effectively separating anomalies induced by models with small depth differences. In addition, when comparing the noise resistance of low-rank methods with existing potential field separation methods using synthetic data, the results show that low-rank methods can extract effective signals from signals contaminated by sparse noise and periodic noise. We then apply this method to extract local gravity anomalies caused by intrusive rocks in the Nanling region and effectively identify gravity anomalies associated with various intrusive rocks. This method facilitates the separation of gravity and magnetic anomalies originating from sources at both different and similar depths, thereby expanding the applicability of separation techniques and enhancing the resolution of gravity and magnetic detection.","PeriodicalId":13213,"journal":{"name":"IEEE Transactions on Geoscience and Remote Sensing","volume":"63 ","pages":"1-19"},"PeriodicalIF":7.5,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143836735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Semi-Supervised Change Detection With Boundary Refinement Teacher 基于边界细化的半监督变化检测

IF 8.2 1区地球科学

IEEE Transactions on Geoscience and Remote Sensing Pub Date : 2025-04-15 DOI: 10.1109/tgrs.2025.3560724

You Su, Yonghong Song, Xiaomeng Wu, Hao Hu, Jingqi Chen, Zehan Wen

引用次数: 0

Learning Temporal Consistency for High Spatial Resolution Remote Sensing Imagery Semantic Change Detection 基于时间一致性学习的高空间分辨率遥感图像语义变化检测

IF 8.2 1区地球科学

IEEE Transactions on Geoscience and Remote Sensing Pub Date : 2025-04-15 DOI: 10.1109/tgrs.2025.3561021

Shiqi Tian, Ailong Ma, Zhuo Zheng, Xicheng Tan, Yanfei Zhong

引用次数: 0