IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing最新文献_第4页

A Multistage Detection Framework Based on TFA and Multiframe Correlation for HFSWR

IF 4.7 2区地球科学

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2025-01-09 DOI: 10.1109/JSTARS.2025.3527468

Zongtai Li;Gangsheng Li;Ling Zhang;Lanjun Liu;Q. M. Jonathan Wu

{"title":"A Multistage Detection Framework Based on TFA and Multiframe Correlation for HFSWR","authors":"Zongtai Li;Gangsheng Li;Ling Zhang;Lanjun Liu;Q. M. Jonathan Wu","doi":"10.1109/JSTARS.2025.3527468","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3527468","url":null,"abstract":"Maritime surveillance heavily relies on high-frequency surface wave radar (HFSWR) systems. However, clutter and interference make it difficult to accurately detect vessel targets using a single-frame detection method. This study introduces an improved time-frequency analysis (TFA) algorithm to enhance the features in single-frame detection. In this article, TFA, multiframe correlation, and deep neural networks are integrated to develop a three-stage detection framework. First, faster R-CNN is customized for the preprocessing stage to identify sea clutter regions. Then, based on the range-Doppler (RD) spectrum, suspicious targets are swiftly identified amidst clutter in the initial stage. Subsequently, the improved TFA algorithm is applied to adjacent range cells of suspicious targets to generate multiframe TF images, forming a three-dimensional data block structured as time-RD frequency. To reduce computational complexity, a TFA method using multisynchrosqueezing transform is employed, enhancing detection accuracy for targets within cluttered regions. In the final stage, a 3DResnet model is utilized to leverage the differences in features between clutter and targets across three dimensions. This allows for distinguishing genuine targets from false targets using time series information from multiple frames. Comparative analysis against classical target detection algorithms demonstrates the superior detection performance of the proposed framework within clutter regions. This showcases its potential for enhancing the maritime surveillance capabilities of HFSWR.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"3893-3904"},"PeriodicalIF":4.7,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10834597","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Multifeature Fusion Network for Tree Species Classification Based on Ground-Based LiDAR Data

IF 4.7 2区地球科学

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2025-01-09 DOI: 10.1109/JSTARS.2025.3527808

Yaoting Liu;Yiming Chen;Zhengjun Liu;Jianchang Chen;Yuxuan Liu

{"title":"A Multifeature Fusion Network for Tree Species Classification Based on Ground-Based LiDAR Data","authors":"Yaoting Liu;Yiming Chen;Zhengjun Liu;Jianchang Chen;Yuxuan Liu","doi":"10.1109/JSTARS.2025.3527808","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3527808","url":null,"abstract":"Light detection and ranging (LiDAR) holds considerable promise for tree species classification. Existing networks that utilize point clouds of individual trees have shown promising results. However, challenges, such as incomplete point cloud data, uneven point density across different components of the tree, and complex tree morphologies, can hinder classification accuracy. To overcome these limitations, we introduced the multifeature fusion tree classifier network (MFFTC-Net). This network leverages a novel boundary-driven point sampling method that preserves more canopy points and mitigates the effects of uneven point density. We also utilize the umbrella-repSurf module, which captures local geometric features and enhances the model's responsiveness to tree structural nuances. The backbone of MFFTC-Net integrates these innovations through a multifeature fusion approach, utilizing set abstraction for local information capture and transformer-based feature interaction for robust multiscale feature integration. Our results demonstrate that MFFTC-Net significantly outperforms other state-of-the-art methods in LiDAR-based tree species classification, achieving the highest overall accuracy and kappa coefficients on both a self-built dataset of four species and a public dataset of seven species.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"4649-4664"},"PeriodicalIF":4.7,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10834575","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106080","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Semantic-Injected Bidirectional Multiscale Flow Estimation Network for Infrared and Visible Image Registration

IF 4.7 2区地球科学

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2025-01-08 DOI: 10.1109/JSTARS.2025.3527175

Chunna Tian;Liuwei Xu;Xiangyang Li;Heng Zhou;Xiqun Song

{"title":"Semantic-Injected Bidirectional Multiscale Flow Estimation Network for Infrared and Visible Image Registration","authors":"Chunna Tian;Liuwei Xu;Xiangyang Li;Heng Zhou;Xiqun Song","doi":"10.1109/JSTARS.2025.3527175","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3527175","url":null,"abstract":"Infrared and visible image registration ensures consistency in spatial positions across different modalities. Cross-modal images contain different scales objects and cluttered backgrounds. However, most existing image registration methods adopt the same alignment strategy for different objects, which leads to insufficient multiscale feature representation and inaccurate registration of foreground objects. To address these issues, we propose a semantic-injected bidirectional multiscale flow estimation (SI-BMFE) network for infrared and visible image registration. SI-BMFE leverages feature complementarity across different scales and employs a pretrained segmentation network to extract the spatial positions of foreground objects to improve registration accuracy. Specifically, we first design a bidirectional multiscale feature enhancement (BMFE) module to integrate feature complementarity across different scales, effectively extracts both global structures and local details. BMFE pushes the network to roughly align infrared and visible images. Then, the semantic-injected flow estimation (SFE) module is introduced to estimate multilevel deformation fields for fine-grained registration of different objects. SFE utilizes a pretrained segmentation network to obtain spatial location information of foreground objects. Object location cues help the network distinguish and focus on different foreground objects from the background. SFE exploits semantic knowledge to promote fine alignment of different foreground objects and improve the accuracy of cross-modal image registration. Extensive experiments demonstrate that our proposed method outperforms state-of-the-art registration networks on both the MSRS and RoadScene infrared and visible image registration datasets.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"3686-3695"},"PeriodicalIF":4.7,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10833818","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143105981","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Fine-Grained Aircraft Target Recognition Algorithm for Remote Sensing Images Based on YOLOV8

IF 4.7 2区地球科学

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2025-01-08 DOI: 10.1109/JSTARS.2025.3526982

Xiao-Nan Jiang;Xiang-Qian Niu;Fan-Lu Wu;Yao Fu;He Bao;Yan-Chao Fan;Yu Zhang;Jun-Yan Pei

{"title":"A Fine-Grained Aircraft Target Recognition Algorithm for Remote Sensing Images Based on YOLOV8","authors":"Xiao-Nan Jiang;Xiang-Qian Niu;Fan-Lu Wu;Yao Fu;He Bao;Yan-Chao Fan;Yu Zhang;Jun-Yan Pei","doi":"10.1109/JSTARS.2025.3526982","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3526982","url":null,"abstract":"Fine-grained recognition plays a pivotal role in the field of remote sensing image analysis, particularly in critical applications such as reconnaissance and early warning, intelligence analysis, and intelligent interpretation. However, the extensive coverage of remote sensing images, the low pixel ratio of targets, and the subtlety of features pose significant challenges for fine-grained recognition of aircraft targets. This article addresses the issues of missed and false detections in existing aircraft target fine-grained recognition algorithms for remote sensing images by proposing an improved algorithm based on YOLOv8, called FD-YOLOv8 (Focus Detail-YOLOv8). Initially, this article designs a local detail feature module to tackle the problem of information loss in shallow networks. This module enhances the capture of semantic information while extracting shallow features, thereby preserving more fine-grained features and improving the network's feature extraction capability. Subsequently, a focus modulation mechanism is employed to enhance the network's interactive understanding of local and global features, thereby improving the recognition accuracy for small and challenging targets. Finally, a multitype feature fusion is designed, which optimizes the generation of feature maps by integrating local features, high-level semantic information, and low-level texture information, enhancing the accuracy of fine-grained target recognition. Experiments conducted on the public remote sensing image dataset FAIR1M demonstrated that the YOLOv8n algorithm achieved a mean average precision (mAP) of 81.8% for aircraft category recognition tasks. In contrast, FD-YOLOv8 exhibited superior performance, with an mAP of 85.0%, indicating a significant advantage in fine-grained recognition.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"4060-4073"},"PeriodicalIF":4.7,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10833740","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106070","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

LMF-Net: A Learnable Multimodal Fusion Network for Semantic Segmentation of Remote Sensing Data

IF 4.7 2区地球科学

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2025-01-08 DOI: 10.1109/JSTARS.2025.3527213

Jihao Li;Wenkai Zhang;Weihang Zhang;Ruixue Zhou;Chongyang Li;Boyuan Tong;Xian Sun;Kun Fu

{"title":"LMF-Net: A Learnable Multimodal Fusion Network for Semantic Segmentation of Remote Sensing Data","authors":"Jihao Li;Wenkai Zhang;Weihang Zhang;Ruixue Zhou;Chongyang Li;Boyuan Tong;Xian Sun;Kun Fu","doi":"10.1109/JSTARS.2025.3527213","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3527213","url":null,"abstract":"Semantic segmentation of remote sensing images has produced a significant effect on many applications, such as land cover, land use, and smoke detection. With the ever-growing remote sensing data, fusing multimodal data from different sensors is a feasible and effective scheme for semantic segmentation task. Deep learning technology has prominently promoted the development of semantic segmentation. However, the majority of current approaches commonly focus more on feature mixing and construct relatively complex architectures. The further mining for cross-modal features is comparatively insufficient in heterogeneous data fusion. In addition, complex structures also lead to relatively heavy computation burden. Therefore, in this article, we propose an end-to-end learnable multimodal fusion network (LMF-Net) for remote sensing semantic segmentation. Concretely, we first develop a multiscale pooling fusion module by leveraging pooling operator. It provides key-value pairs with multimodal complementary information in a parameter-free manner and assigns them to self-attention (SA) layers of different modal branches. Then, to further harness the cross-modal collaborative embeddings/features, we elaborate two learnable fusion modules, learnable embedding fusion and learnable feature fusion. They are able to dynamically adjust the collaborative relationships of different modal embeddings and features in a learnable approach, respectively. Experiments on two well-established benchmark datasets reveal that our LMF-Net possesses superior segmentation behavior and strong generalization capability. In terms of computation complexity, it achieves competitive performance as well. Ultimately, the contribution of each component involved in LMF-Net is evaluated and discussed in detail.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"3905-3920"},"PeriodicalIF":4.7,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10833730","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106104","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Onboard Processing of Hyperspectral Imagery: Deep Learning Advancements, Methodologies, Challenges, and Emerging Trends

IF 4.7 2区地球科学

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2025-01-08 DOI: 10.1109/JSTARS.2025.3527898

Nafiseh Ghasemi;Jon Alvarez Justo;Marco Celesti;Laurent Despoisse;Jens Nieke

{"title":"Onboard Processing of Hyperspectral Imagery: Deep Learning Advancements, Methodologies, Challenges, and Emerging Trends","authors":"Nafiseh Ghasemi;Jon Alvarez Justo;Marco Celesti;Laurent Despoisse;Jens Nieke","doi":"10.1109/JSTARS.2025.3527898","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3527898","url":null,"abstract":"Recent advancements in deep learning techniques have spurred considerable interest in their application to hyperspectral imagery processing. This article provides a comprehensive review of the latest developments in this field, focusing on methodologies, challenges, and emerging trends. Deep learning architectures, such as convolutional neural networks (CNNs), autoencoders, deep belief networks, generative adverserial networks (GANs), and recurrent neural networks are examined for their suitability in processing hyperspectral data. Key challenges, including limited training data and computational constraints, are identified, along with strategies, such as data augmentation and noise reduction using GANs. This article discusses the efficacy of different network architectures, highlighting the advantages of lightweight CNN models and 1D-CNNs for onboard processing. Moreover, the potential of hardware accelerators, particularly field programmable gate arrays, for enhancing processing efficiency is explored. This review concludes with insights into ongoing research trends, including the integration of deep learning techniques into Earth observation missions, such as the Copernicus hyperspectral imaging mission for the environment mission, and emphasizes the need for further exploration and refinement of deep learning methodologies to address the evolving demands of hyperspectral image processing.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"4780-4790"},"PeriodicalIF":4.7,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10834581","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

TSMGA: Temporal-Spatial Multiscale Graph Attention Network for Remote Sensing Change Detection

IF 4.7 2区地球科学

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2025-01-07 DOI: 10.1109/JSTARS.2025.3526785

Xiaoyang Zhang;Genji Yuan;Zhen Hua;Jinjiang Li

{"title":"TSMGA: Temporal-Spatial Multiscale Graph Attention Network for Remote Sensing Change Detection","authors":"Xiaoyang Zhang;Genji Yuan;Zhen Hua;Jinjiang Li","doi":"10.1109/JSTARS.2025.3526785","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3526785","url":null,"abstract":"In the field of remote sensing change detection, accurately capturing temporal change information and efficiently integrating multilevel information is a major challenge. In order to extend the sensory domain and optimize the information fusion, the model is able to capture temporal-spatial change features more accurately and improve the accuracy of change detection. In this article, we propose a temporal-spatial multiscale graph attention network (TSMGA), specifically, TSMGA employs a pair of pretrained ResNet18 for effective multiscale feature extraction, and in order to enhance the disparity information of the bitemporal images, we also design the temporal fusion block to emphasize the changed areas. The spatial and channel features of multiscale disparity features are enhanced by multiscale spatial-channel aggregation module. To enable more robust and efficient exploration of more global contextual information, we are inspired to introduce shortest path graph attention, which allows for a more informative and complete exploration of the global context, and furthermore allows for more efficient gathering information from far-off neighbors to the central node. In order to ensure comprehensive utilization of local and global features and to significantly improve the clarity and detail retention of the output image, global context residual fusion module (GCRFM) is designed, GCRFM efficiently utilizes the complementary information of the feature maps for fusing and recovering spatial details and variation information. We validate the effectiveness and advancement of TSMGA on three classical datasets (LEVIR-CD, WHU-CD, GZ-CD), and the experimental results show that TSMGA achieves the state-of-the-art performance level.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"3696-3712"},"PeriodicalIF":4.7,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10829977","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143105982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Estimating Seafloor Topography of the South China Sea Using SWOT Wide-Swath Altimetry Data

IF 4.7 2区地球科学

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2025-01-07 DOI: 10.1109/JSTARS.2025.3526683

Fengshun Zhu;Jinbo Li;Yang Li;Jianqiao Xu;Jinyun Guo;Jiangcun Zhou;Heping Sun

{"title":"Estimating Seafloor Topography of the South China Sea Using SWOT Wide-Swath Altimetry Data","authors":"Fengshun Zhu;Jinbo Li;Yang Li;Jianqiao Xu;Jinyun Guo;Jiangcun Zhou;Heping Sun","doi":"10.1109/JSTARS.2025.3526683","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3526683","url":null,"abstract":"The surface water and ocean topography (SWOT) wide-swath altimetry satellite was launched in December 2022. The performance of novel wide-swath altimetry in seafloor topography modeling needs to be evaluated. This study utilized 15 cycles of SWOT Level-3 product to construct seafloor topography model of the South China Sea by linear regression analysis. The root mean square error of the difference between the model and shipborne bathymetry at checkpoints is about 120 m, which is 20 m better than topo_27.1 and DTU18BAT, and 40 m better than ETOPO1. First, the effects of the shipborne bathymetry at control points and priori bathymetry model in different topography-gravity scaling factor estimation strategies [A: using robust least squares (RBLSQ) to estimate regional scaling factor; B: using ratio method to calculate scaling factors at control points; C: using the moving window method and RBLSQ to obtain scaling factor grids.] on SWOT seafloor topography modeling are explored. We find that the control point number barely affects strategy A/C but significantly affects strategy B, while the priori bathymetry model mainly affects strategy C. Then, the three strategies are applied to the traditional radar altimetry gravity anomaly, and the results are compared with the SWOT-derived seafloor topography. The results show that incorporating SWOT data can improve the accuracy of seafloor topography estimation by about 7 m, and improve the power spectral density in the wavelength range about 10–20 km, which can help to reveal more detailed topography information.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"3569-3580"},"PeriodicalIF":4.7,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10829941","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106102","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dual-Granularity Feature Alignment for Change Detection in Remote Sensing Images

IF 4.7 2区地球科学

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2025-01-07 DOI: 10.1109/JSTARS.2025.3526795

Feng Zhou;Xinyu Zhang;Hui Shuai;Renlong Hang;Shanshan Zhu;Tianyu Geng

{"title":"Dual-Granularity Feature Alignment for Change Detection in Remote Sensing Images","authors":"Feng Zhou;Xinyu Zhang;Hui Shuai;Renlong Hang;Shanshan Zhu;Tianyu Geng","doi":"10.1109/JSTARS.2025.3526795","DOIUrl":"https://doi.org/10.1109/JSTARS.2025.3526795","url":null,"abstract":"Deep learning has emerged as the preferred method for remote sensing change detection owing to its ability to automatically extract discriminative features from bitemporal images. However, few methods simultaneously consider heterogeneous appearance of objects and affine geometric difference between bitemporal images, both of which contribute to pseudochange. In this article, dual-granularity feature alignment (DgFA) is proposed to deal with these two issues. Specifically, bitemporal features extracted by transformer, along with learnable class tokens, are input into the proposed semantic alignment module to adjust the appearance of separate instances from same-category objects to ensure a cohesive style. Then, a spatial alignment module is introduced to use the estimated transformation field to accomplish bitemporal feature registration. Finally, we develop a temporal contrast-based change detection head to infer the change map based on dual-granularity aligned bitemporal features and corresponding difference maps. To refine the change map, this head also constrains the feature similarity within changed and unchanged regions across bitemporal features via a contrastive loss. Experimental results demonstrate that DgFA outperforms several state-of-the-art methods on three public benchmark datasets, including LEVIR-CD, CDD, and SYSU-CD.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"4487-4497"},"PeriodicalIF":4.7,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10830007","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143105954","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Apparent Thermal Inertia Based Trapezoid Model for Downscaling ESA CCI Soil Moisture Products

IF 4.7 2区地球科学

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Pub Date : 2025-01-07 DOI: 10.1109/JSTARS.2024.3525305

Shulin Li;Minfeng Xing;Taifeng Dong

{"title":"An Apparent Thermal Inertia Based Trapezoid Model for Downscaling ESA CCI Soil Moisture Products","authors":"Shulin Li;Minfeng Xing;Taifeng Dong","doi":"10.1109/JSTARS.2024.3525305","DOIUrl":"https://doi.org/10.1109/JSTARS.2024.3525305","url":null,"abstract":"Existing long-term soil moisture (SM) products are relatively coarse in spatial resolution, limiting their applications in heterogeneous scales. Various spectral information derived from optical satellite data, such as the land surface temperature-vegetation parameter (LST-VP), have been widely employed to detect spatiotemporal variability of SM under different regional hydrological scales. In this study, inspired by the concept of LST-VI space, an ATI-VP (apparent thermal inertia-vegetation parameter) was proposed and assessed for downscaling the ESA CCI SM product from 25 to 1 km. Different vegetation indices (including NDVI, EVI, NIRv, and MSAVI) and biophysical variables (LAI and fPAR) derived from MODIS satellites were first assessed as inputs of the ATI-VP space to estimate AVDI (apparent thermal inertia/vegetation drought index). The AVDI was then applied to the weight decomposition model for SM downscaling. Overall, LAI for the ATI-VP space achieved the best AVDI performance. The accuracy of SM estimation was validated using in situ SM collected from the Murrumbidgee soil moisture monitoring network. The results showed that the accuracy of the downscaled 1 km SM (R = 0.637, bias = 0.038 m3/m3) was close to that of the CCI SM (R = 0.661, bias = 0.030 m3/m3). However, the downscaled SM data exhibited enhanced spatial detail compared to CCI SM data. Further analysis based on the time series SM indicated that both the CCI SM and the downscaled SM are in good agreement in terms of temporal evolution. The downscaling method shows high potential for application in SM mapping across semiarid regions.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"4473-4486"},"PeriodicalIF":4.7,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10829995","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106060","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0