IEEE Transactions on Geoscience and Remote Sensing最新文献

{"title":"Realistic Simulation of Underwater Scene for Image Enhancement","authors":"Songyang Li;Tingyu Liu;Qunyan Jiang;Yuanqi Li;Jie Guo;Lei Jiao;Yanwen Guo;Zhonghua Ni","doi":"10.1109/TGRS.2025.3561927","DOIUrl":"10.1109/TGRS.2025.3561927","url":null,"abstract":"In recent years, learning-based methods have performed remarkably well in underwater image enhancement (UIE), but their performance is limited by the lack of high-quality, diverse training datasets. Current underwater image datasets are unable to address the following three issues: intradomain gaps in underwater environments, interdomain gaps between synthetic and real data, and domain inaccuracies. To overcome these limitations, we construct a realistic underwater scene using 3-D graphics engine through a three-step approach: 1) integrate a simulation-specific underwater light propagation models to create volumetric fog; 2) employ physical model-based rendering for accurate light field simulation; and 3) configure scenes with parameters extracted from real underwater images. Based on this framework, we develop an UIE dataset [model-based underwater synthetic environment (MUSE)]. Experiments demonstrate that models trained on MUSE outperform those trained on conventional datasets, highlighting the effectiveness of our approach.","PeriodicalId":13213,"journal":{"name":"IEEE Transactions on Geoscience and Remote Sensing","volume":"63 ","pages":"1-14"},"PeriodicalIF":7.5,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143847118","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}

{"title":"Extended Target Reconstruction for Real Aperture Radar Using Sparse and Two-dimensional High-order Gradient Hybrid Prior Bayesian Method","authors":"Jiahao Shen, Yin Zhang, Deqing Mao, Yulin Huang, Jianyu Yang","doi":"10.1109/tgrs.2025.3561856","DOIUrl":"https://doi.org/10.1109/tgrs.2025.3561856","url":null,"abstract":"","PeriodicalId":13213,"journal":{"name":"IEEE Transactions on Geoscience and Remote Sensing","volume":"30 1","pages":""},"PeriodicalIF":8.2,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143847122","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}

{"title":"Handling Noisy Annotation for Remote Sensing Semantic Segmentation via Boundary-aware Knowledge Distillation","authors":"Yue Sun, Dong Liang, Shaoyuan Li, Songcan Chen, Sheng-Jun Huang","doi":"10.1109/tgrs.2025.3562073","DOIUrl":"https://doi.org/10.1109/tgrs.2025.3562073","url":null,"abstract":"","PeriodicalId":13213,"journal":{"name":"IEEE Transactions on Geoscience and Remote Sensing","volume":"17 1","pages":""},"PeriodicalIF":8.2,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143847119","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}

{"title":"HyperKING: Quantum-Classical Generative Adversarial Networks for Hyperspectral Image Restoration","authors":"Chia-Hsiang Lin, Si-Sheng Young","doi":"10.1109/tgrs.2025.3561951","DOIUrl":"https://doi.org/10.1109/tgrs.2025.3561951","url":null,"abstract":"","PeriodicalId":13213,"journal":{"name":"IEEE Transactions on Geoscience and Remote Sensing","volume":"9 1","pages":""},"PeriodicalIF":8.2,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143847123","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}

{"title":"Adaptive Homophily Clustering: Structure Homophily Graph Learning With Adaptive Filter for Hyperspectral Image","authors":"Yao Ding;Zhili Zhang;Weijie Kang;Aitao Yang;Junyang Zhao;Jie Feng;Danfeng Hong;Qinghe Zheng","doi":"10.1109/TGRS.2025.3556276","DOIUrl":"https://doi.org/10.1109/TGRS.2025.3556276","url":null,"abstract":"Hyperspectral image (HSI) clustering is a fundamental yet challenging task that typically operates without training labels. Recent advancements in deep graph clustering methods have shown promise for HSI due to their ability to effectively encode spatial structural information. However, limitations such as inadequate utilization of structural information, poor feature representation, and weak graph update capabilities hinder their performance. In this article, we propose an adaptive homophily structure graph clustering (AHSGC) method for HSI. Our approach begins with the generation of homogeneous regions to process HSI and construct the initial graph. Next, we design an adaptive filter graph encoder that captures both high and low-frequency features for subsequent processing. We then develop a graph embedding clustering self-training decoder using KL Divergence to generate pseudo-labels for network training. To enhance graph learning, we introduce homophily-enhanced structure learning, which updates the graph based on the clustering task. This involves estimating node connections through orient correlation estimation and dynamically adjusting graph edges via graph edge sparsification. Finally, we implement joint network optimization to facilitate self-training and graph updates, with K-means used to express latent features. The clustering accuracy on three datasets is 83.60%, 63.65%, and 86.03%, the FLOPs are 3.57G, 30.62G, and 2.95G. The source code will be available at <uri>https://github.com/DY-HYX</uri>.","PeriodicalId":13213,"journal":{"name":"IEEE Transactions on Geoscience and Remote Sensing","volume":"63 ","pages":"1-13"},"PeriodicalIF":7.5,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143845437","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}

{"title":"RoSENet: Rotation and Similarity Enhancement Network for Multimodal Remote Sensing Image Land Cover Classification","authors":"Bokun Ma, Caihong Mu, Yi Liu, Xinyu He, Mosa Haidarh","doi":"10.1109/tgrs.2025.3561850","DOIUrl":"https://doi.org/10.1109/tgrs.2025.3561850","url":null,"abstract":"","PeriodicalId":13213,"journal":{"name":"IEEE Transactions on Geoscience and Remote Sensing","volume":"9 1","pages":""},"PeriodicalIF":8.2,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143847120","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}

{"title":"Understanding the Multiscale Relationships Between Grain Yields of Maize in China and Influencing Factors via Multiscale Geographically Weighted Regression Model","authors":"Yuxue Wang;Lili Huo;Yi An;Bingbo Gao;Yelu Zeng;Jianyu Yang;Quanlong Feng;Xiaochuang Yao;Yuanyuan Zhao","doi":"10.1109/TGRS.2025.3561853","DOIUrl":"10.1109/TGRS.2025.3561853","url":null,"abstract":"Maize is a key global food crop, with China being a major producer vital for global maize supply and food security. Accurately analyzing the relationships between grain yields of maize and influencing factors is crucial for enhancing crop production, evaluating arable land quality, and optimizing planting structure. However, when modeling those relationships, the coefficients of each influencing factor vary spatially and have different spatial scales, suggesting that the importance of the influencing factors is multiscale. Traditional global and local modeling methods such as ordinary least squares (OLS) and geographically weighted regression (GWR) models cannot accurately explain those multiscale spatial relationships. The multiscale GWR (MGWR) model, an extension of GWR, addresses these limitations by allowing each explanatory variable to have a unique spatial scale. By aligning the neighborhood structure of each variable with its corresponding spatial scale, MGWR improves the accuracy of local regression coefficient estimations, providing a more refined analysis of spatial heterogeneity. In this article, the multiscale importance of influencing factors on maize grain yield of the Chinese mainland was apportioned via MGWR model. Our findings verified that the relationships between maize grain yield and influencing factors differ at multiple spatial scales. The MGWR model can comprehensively apportion the importance of influencing factors at multiple scale, while global and local modeling methods provide biased estimations, with OLS method leaving large residues and GWR model attributing part contribution to spatially varying intercept terms. With the MGWR model, organic fertilizer and terrain aspect are globally important and their relationships with yields keep stationary; the relationships between yields and soil pH value, GDP, digital elevation model (DEM), and slope vary on a medium scale, presenting obvious regional differences; cultivation convenience and hydrothermal conditions affect yields at small scales. The comprehensive apportionment of multiscale relationships is an important guideline for the scientific management of agriculture and arable land resources.","PeriodicalId":13213,"journal":{"name":"IEEE Transactions on Geoscience and Remote Sensing","volume":"63 ","pages":"1-17"},"PeriodicalIF":7.5,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143847121","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}

{"title":"Frequency and Spatial-domain Saliency Network for Remote Sensing Cross-Modal Retrieval","authors":"Chengyu Zheng, Jie Nie, Bo Yin, Xiu Li, Yuntao Qian, Zhiqiang Wei","doi":"10.1109/tgrs.2025.3561626","DOIUrl":"https://doi.org/10.1109/tgrs.2025.3561626","url":null,"abstract":"","PeriodicalId":13213,"journal":{"name":"IEEE Transactions on Geoscience and Remote Sensing","volume":"5 1","pages":""},"PeriodicalIF":8.2,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143841889","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}

{"title":"SMPD-MERG: a hybrid downscaling model for high-resolution daily precipitation estimation via merging surface soil moisture and multi-source precipitation data","authors":"Kunlong He, Wei Zhao, Luca Brocca, Pere Quintana-Seguí, Xiaohong Chen","doi":"10.1109/tgrs.2025.3561253","DOIUrl":"https://doi.org/10.1109/tgrs.2025.3561253","url":null,"abstract":"","PeriodicalId":13213,"journal":{"name":"IEEE Transactions on Geoscience and Remote Sensing","volume":"17 1","pages":""},"PeriodicalIF":8.2,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143841574","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}

{"title":"Enhancing Martian Terrain Recognition with Deep Constrained Clustering","authors":"Tejas Panambur, Mario Parente","doi":"10.1109/tgrs.2025.3561687","DOIUrl":"https://doi.org/10.1109/tgrs.2025.3561687","url":null,"abstract":"","PeriodicalId":13213,"journal":{"name":"IEEE Transactions on Geoscience and Remote Sensing","volume":"108 1","pages":""},"PeriodicalIF":8.2,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143841839","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}