IEEE Transactions on Geoscience and Remote Sensing最新文献

{"title":"Deep Learning Inversion of Ground Penetrating Radar with Prior Physical Velocity Field for Complex Subsurface Root System","authors":"Xiaowei Zhang, Xuan Zhao, Shenghua Lv, Lingfei Xv, Chen Lin, Jian Wen","doi":"10.1109/tgrs.2025.3553688","DOIUrl":"https://doi.org/10.1109/tgrs.2025.3553688","url":null,"abstract":"","PeriodicalId":13213,"journal":{"name":"IEEE Transactions on Geoscience and Remote Sensing","volume":"24 1","pages":""},"PeriodicalIF":8.2,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143672489","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":"Parameter-Free Spectral-Spatial Optimization Algorithm for Semi-Blind Hyperspectral and Multispectral Image Fusion","authors":"Jian Long, Jialin Gui, Yuanxi Peng, Yibing Zhan, Jun Li, Quan Zhang, Yulei Tian","doi":"10.1109/tgrs.2025.3553445","DOIUrl":"https://doi.org/10.1109/tgrs.2025.3553445","url":null,"abstract":"","PeriodicalId":13213,"journal":{"name":"IEEE Transactions on Geoscience and Remote Sensing","volume":"34 1","pages":""},"PeriodicalIF":8.2,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143672357","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":"SiMultiF: A Remote Sensing Multimodal Semantic Segmentation Network With Adaptive Allocation of Modal Weights for Siamese Structures in Multiscene","authors":"Shichao Cui;Wei Chen;Wenwu Xiong;Xin Xu;Xinyu Shi;Canhai Li","doi":"10.1109/TGRS.2025.3553713","DOIUrl":"10.1109/TGRS.2025.3553713","url":null,"abstract":"Semantic segmentation of remote sensing images is crucial for resource exploration, precision agriculture, and environmental monitoring. However, conducting semantic segmentation on single-modality data for remote sensing images that contain various scenes, especially unique scenes, is highly challenging. To address this challenge, we propose SiMultiF, a Siamese architecture-based multimodal feature adaptive fusion semantic segmentation network. SiMultiF employs a dual-branch Siamese structure feature extractor. The adaptive feature weight adjustment module (AFWAM) and the multimodal fusion module (MFM) facilitate in-depth understanding and extraction of multimodal data. Specifically, the Siamese structure can extract features from multimodal data concurrently without adding to the number of parameters. The AFWAM module can adaptively identify the importance of different modal data and dynamically adjust the modal weight to enhance the network’s comprehension of complex scene data. Additionally, the cross-attention (CA)-based MFM module bridges modality gaps and achieves comprehensive multimodal feature fusion. Numerous experiments have demonstrated that the proposed SiMultiF outperforms other state-of-the-art semantic segmentation models (both multimodal and single modal) on the high-resolution ISPRS Potsdam dataset, ISPRS Vaihingen dataset, and special scene dataset (vegetation polarization dataset with extreme natural lighting contrast). Moreover, the robustness and generalizability of the network in multiscene and multimodal datasets are verified.","PeriodicalId":13213,"journal":{"name":"IEEE Transactions on Geoscience and Remote Sensing","volume":"63 ","pages":"1-17"},"PeriodicalIF":7.5,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143672490","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":"Dual Prediction-Guided Distillation for Object Detection in Remote Sensing Images","authors":"Ruiqing Wang, Yifan Fu, Yang Xu, Zebin Wu, Zhihui Wei","doi":"10.1109/tgrs.2025.3553128","DOIUrl":"https://doi.org/10.1109/tgrs.2025.3553128","url":null,"abstract":"","PeriodicalId":13213,"journal":{"name":"IEEE Transactions on Geoscience and Remote Sensing","volume":"27 1","pages":""},"PeriodicalIF":8.2,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143672278","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":"P-wave velocity model at Utah FORGE geothermal field using travel-time tomography of DAS-VSP data","authors":"Sea-Eun Park, Nori Nakata, Ju-Won Oh, Ben Dyer","doi":"10.1109/tgrs.2025.3553145","DOIUrl":"https://doi.org/10.1109/tgrs.2025.3553145","url":null,"abstract":"","PeriodicalId":13213,"journal":{"name":"IEEE Transactions on Geoscience and Remote Sensing","volume":"183 1","pages":""},"PeriodicalIF":8.2,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143672279","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":"Intelligent Velocity Azimuth Display (IVAD) Wind Estimation in Clear-Air with S-Band Polarimetric Weather Radars","authors":"Precious Jatau, Tian-You Yu, Valery Melnikov, Jeffrey Kelly","doi":"10.1109/tgrs.2025.3551955","DOIUrl":"https://doi.org/10.1109/tgrs.2025.3551955","url":null,"abstract":"","PeriodicalId":13213,"journal":{"name":"IEEE Transactions on Geoscience and Remote Sensing","volume":"34 1","pages":""},"PeriodicalIF":8.2,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143672276","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":"FTransDeepLab: Multimodal Fusion Transformer-Based DeepLabv3+ for Remote Sensing Semantic Segmentation","authors":"Haixia Feng;Qingwu Hu;Pengcheng Zhao;Shunli Wang;Mingyao Ai;Daoyuan Zheng;Tiancheng Liu","doi":"10.1109/TGRS.2025.3553478","DOIUrl":"10.1109/TGRS.2025.3553478","url":null,"abstract":"High-resolution remote sensing images contain rich color and texture information, but due to the inherent limitations of 2-D data, achieving high-quality semantic segmentation remains a challenge. Multimodal data fusion technology has emerged as an effective approach to overcome this issue. To accurately capture the semantic information in remote sensing images, this study designs a multimodal fusion Transformer-based DeepLabv3+ model for remote sensing semantic segmentation, named FTransDeepLab. Specifically, the network learns features from two modalities and is inspired by the DeepLab architecture. We extended the encoder by stacking the multiscale Segformer, encoding the input images into highly representative spatial features. Additionally, we introduced the multimodal feature rectification (MFR) module and the multimodal feature fusion (MFF) module. The MFR, composed of a channel attention module and a spatial attention module, enhances the model’s ability to capture essential features and improves performance by focusing on both global and local contexts. The MFF module utilizes a cross-attention mechanism to optimize the feature fusion process, which enhances representation learning by facilitating the interaction between diverse information and integrates features from different modalities. Finally, in the decoding path, the extracted high-level features are concatenated with low-level features to optimize the feature representation and upsampled to restore the size of input image. Extensive results on two datasets, the International Society for Photogrammetry and Remote Sensing (ISPRS) Vaihingen and Potsdam, have confirmed that the proposed FTransDeepLab can achieve superior performance compared to the state-of-the-art segmentation methods.","PeriodicalId":13213,"journal":{"name":"IEEE Transactions on Geoscience and Remote Sensing","volume":"63 ","pages":"1-18"},"PeriodicalIF":7.5,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143672544","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":"PGCS: Physical Law Embedded Generative Cloud Synthesis in Remote Sensing Images","authors":"Liying Xu;Huifang Li;Huanfeng Shen;Mingyang Lei;Tao Jiang","doi":"10.1109/TGRS.2025.3553239","DOIUrl":"10.1109/TGRS.2025.3553239","url":null,"abstract":"Data quantity and quality are both critical for information extraction and analyzation in remote sensing. The current remote sensing datasets, however, often fail to meet these two requirements, for which the cloud is a primary factor degrading the data quantity and quality. This limitation affects the precision of results in remote sensing applications, particularly those derived from data-driven techniques. In this article, a physical law embedded generative cloud synthesis (PGCS) method is proposed to generate diverse,ealistic cloud images to enhance real data and promote the development of algorithms for subsequent tasks, such as cloud correction, cloud detection, and data augmentation for classification, recognition, and segmentation. The PGCS method involves two key phases: spatial synthesis and spectral synthesis. In the spatial synthesis phase, a style-based generative adversarial network is used to simulate the spatial characteristics, generating an infinite number of single-channel clouds. In the spectral synthesis phase, the atmospheric scattering law is embedded through a local statistics and global fitting method, converting the single-channel clouds into multispectral clouds. The experimental results demonstrate that PGCS achieves a high accuracy in both phases and performs better than three other existing cloud synthesis methods. Two cloud correction methods are developed from PGCS and exhibits a superior performance compared to state-of-the-art methods in the cloud correction task. The application of PGCS with data from various sensors was, furthermore, investigated and successfully extended. Code will be provided at <uri>https://github.com/Liying-Xu/PGCS</uri>.","PeriodicalId":13213,"journal":{"name":"IEEE Transactions on Geoscience and Remote Sensing","volume":"63 ","pages":"1-17"},"PeriodicalIF":7.5,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143672277","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":"A Lightweight Multiscale and Multiattention Hyperspectral Image Classification Network Based on Multistage Search","authors":"Kefan Li;Yuting Wan;Ailong Ma;Yanfei Zhong","doi":"10.1109/TGRS.2025.3553147","DOIUrl":"10.1109/TGRS.2025.3553147","url":null,"abstract":"Hyperspectral image (HSI) classification has become a core task in hyperspectral remote sensing interpretation, with deep learning dominating due to its ability to learn hierarchical features without manual engineering. As the model complexity has grown, manual design limitations have prompted a shift to automated approaches such as differentiable architecture search (DARTS), where the architectures are optimized for greater accuracy and efficiency. However, applying gradient-based neural architecture search (NAS) methods directly to hyperspectral classification presents several challenges. Regarding search space design, there is a lack of lightweight operators that can mitigate the spectral variability, spatial heterogeneity, and scale differences inherent in hyperspectral imagery. In terms of search strategy, the traditional DARTS approach directly derives the topology from operation weights, which can lead to suboptimal topological structures, and thus affects the performance of the network in HSI classification. In this article, to address these issues, we propose L3M, which is a lightweight multiscale and multiattention HSI classification network based on multistage search. The proposed approach introduces a novel lightweight operator to address the spectral variability, spatial heterogeneity, and scale differences in HSIs. The operation search and topology search are also decomposed into a multistage process to prevent a suboptimal network by searching for and determining the topological order of the candidate operations in a predefined operation space. L3M was validated on four public datasets, where the proposed model demonstrated a superior classification performance, compared to other lightweight models, while maintaining a low parameter count, low model complexity, and high inference speed.","PeriodicalId":13213,"journal":{"name":"IEEE Transactions on Geoscience and Remote Sensing","volume":"63 ","pages":"1-18"},"PeriodicalIF":7.5,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143672315","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":"A Geographically Random Machine Learning Model for GOME-2 Global Seamless Sun-Induced Chlorophyll Fluorescence Downscaling Products With High Spatiotemporal Resolution","authors":"Sicong He;Yanbin Yuan;Heng Dong;Xiufeng Chen;Chengfang Zhang","doi":"10.1109/TGRS.2025.3552678","DOIUrl":"10.1109/TGRS.2025.3552678","url":null,"abstract":"Several downscaled datasets of sun-induced chlorophyll fluorescence (SIF) enhance the quality of raw SIF satellite retrievals and offer a better perspective for monitoring terrestrial ecosystems. They are, however, still under pressure in studies that rely on long-term fine-scale observations, such as drought monitoring, climate capture, and gross primary productivity (GPP) estimation, due to persistent temporal and spatial discrimination deficits, spatial gaps, or nonnegligible numerical errors. These limitations are especially noticeable in GOME-2 and its related datasets. This study presented a new machine learning model [called “Geographically random light gradient boosting machine” (GR-LGBM)] and successfully produced a seamless dataset of global daily-mean SIF (GR-LGBM produced SIF) with a spatiotemporal resolution of 0.01° and 8 days for the period 2008–2018, based on GOME-2 SIF, visible-near-infrared reflectance, meteorological, and radiological variables. GR-LGBM performed well in the task of establishing the mapping relationship between the original SIF retrieval and the explanatory variables (fivefold cross-validated <inline-formula> <tex-math>${R} ^{2}$ </tex-math></inline-formula>: 0.885), outperforming widely used machine learning models such as extreme random forest (EXT), light gradient boosting machine (LGBM), random forest (RF), categorical boosting (CatBoost), Cubist, eXtreme gradient boosting (XGBoost), neural network (NN), and possesses good temporal prediction ability and spatial robustness. The GRSIF001 was highly consistent with SIF satellite retrievals from GOME-2, OCO-2, TROPOMI, and ground-based SIF site observations, demonstrating the spatiotemporal accuracy and temporal scalability of GRSIF001. Additionally, evidence that GRSIF001 precisely captured the actual photosynthesis in vegetation was presented. The downscaling model is better suited for characterizing spatial and temporal heterogeneity of geographic variables, and GRSIF001 will provide greater value in accurately understanding the dynamics of terrestrial photosynthesis.","PeriodicalId":13213,"journal":{"name":"IEEE Transactions on Geoscience and Remote Sensing","volume":"63 ","pages":"1-15"},"PeriodicalIF":7.5,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143672359","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}