Pub Date : 2024-12-17DOI: 10.1109/LGRS.2024.3516854
Guangxia Wang;Kuiliang Gao;Xiong You
Multimodal land cover classification (LCC) of optical and SAR images has become a research hotspot. However, there are still two unsolved problems: the lack of a deep fusion mechanism and the neglect of the diversity of multimodal features. Inspired by ensemble learning, this letter proposes the cascaded multimodal forest-of-experts (CM2FEs) for deeper and broader fusion to further improve the performance of LCC. The proposed method first establishes the expert tree, then combines multiple trees at the same level into a forest, and finally forms a cascaded forest across different levels. Specifically, the novel designs include three points: 1) the multimodal expert tree is built based on linear projection and dynamic routing, with multiple layers of experts; it can acquire more discriminative multimodal features through deeper fusion; 2) the cascaded forest is formed by combining expert trees at the same level and different levels, which can effectively ensemble the knowledge learned by different trees; it can generate more diverse multimodal features through broader fusion; and 3) two expert exchange strategies are proposed to transfer knowledge between different trees and further optimize the feature fusion effect. Experiments show that the proposed method performs better than existing methods, and the mean IoU (mIoU) has been improved by at least 1.60%–3.25%.
{"title":"Deeper and Broader Multimodal Fusion: Cascaded Forest-of-Experts for Land Cover Classification","authors":"Guangxia Wang;Kuiliang Gao;Xiong You","doi":"10.1109/LGRS.2024.3516854","DOIUrl":"https://doi.org/10.1109/LGRS.2024.3516854","url":null,"abstract":"Multimodal land cover classification (LCC) of optical and SAR images has become a research hotspot. However, there are still two unsolved problems: the lack of a deep fusion mechanism and the neglect of the diversity of multimodal features. Inspired by ensemble learning, this letter proposes the cascaded multimodal forest-of-experts (CM2FEs) for deeper and broader fusion to further improve the performance of LCC. The proposed method first establishes the expert tree, then combines multiple trees at the same level into a forest, and finally forms a cascaded forest across different levels. Specifically, the novel designs include three points: 1) the multimodal expert tree is built based on linear projection and dynamic routing, with multiple layers of experts; it can acquire more discriminative multimodal features through deeper fusion; 2) the cascaded forest is formed by combining expert trees at the same level and different levels, which can effectively ensemble the knowledge learned by different trees; it can generate more diverse multimodal features through broader fusion; and 3) two expert exchange strategies are proposed to transfer knowledge between different trees and further optimize the feature fusion effect. Experiments show that the proposed method performs better than existing methods, and the mean IoU (mIoU) has been improved by at least 1.60%–3.25%.","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":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142858948","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}
Pub Date : 2024-12-12DOI: 10.1109/LGRS.2024.3516004
Ruoying Yin;Wei Han
Hyperspectral infrared (IR) sounders on board geostationary (GEO) satellite, represented by the GEO interferometric IR sounder (GIIRS), can provide unprecedented and valuable observations with high-temporal resolution for disaster prevention, mitigation, and meteorological support services. FengYun-4B (FY-4B) GIIRS has carried out targeted sounding observations with a temporal resolution of 15 min for two super typhoons in 2024 (Typhoon Gaemi and Typhoon Yagi). In this study, an operational parallel experiment system was established to assimilate the FY-4B GIIRS targeted radiances in real time, and the diagnosis and analysis of typhoon forecast were carried out after the typhoon dissipated. The results indicate that assimilating FY-4B GIIRS can improve the track forecast of the two super typhoons in real-time operational environment, and the improvement is more significant after 60-h forecast. The average track forecast was increased by 22.5% in Typhoon Gaemi and by 6.3% in Typhoon Yagi, although the impact on typhoon intensity forecast was not significant. Additionally, the difference in the number of clear sky points assimilated in the two case experiments shows the importance of cloud sky assimilation in the future. This study reveals the potential and value of FY-4B GIIRS quantitative assimilation application to improve numerical weather prediction (NWP) skills, especially high-impact weather (HIW) events.
{"title":"Impact of Targeted Sounding Observations From FY-4B GIIRS on Two Super Typhoon Forecasts in 2024","authors":"Ruoying Yin;Wei Han","doi":"10.1109/LGRS.2024.3516004","DOIUrl":"https://doi.org/10.1109/LGRS.2024.3516004","url":null,"abstract":"Hyperspectral infrared (IR) sounders on board geostationary (GEO) satellite, represented by the GEO interferometric IR sounder (GIIRS), can provide unprecedented and valuable observations with high-temporal resolution for disaster prevention, mitigation, and meteorological support services. FengYun-4B (FY-4B) GIIRS has carried out targeted sounding observations with a temporal resolution of 15 min for two super typhoons in 2024 (Typhoon Gaemi and Typhoon Yagi). In this study, an operational parallel experiment system was established to assimilate the FY-4B GIIRS targeted radiances in real time, and the diagnosis and analysis of typhoon forecast were carried out after the typhoon dissipated. The results indicate that assimilating FY-4B GIIRS can improve the track forecast of the two super typhoons in real-time operational environment, and the improvement is more significant after 60-h forecast. The average track forecast was increased by 22.5% in Typhoon Gaemi and by 6.3% in Typhoon Yagi, although the impact on typhoon intensity forecast was not significant. Additionally, the difference in the number of clear sky points assimilated in the two case experiments shows the importance of cloud sky assimilation in the future. This study reveals the potential and value of FY-4B GIIRS quantitative assimilation application to improve numerical weather prediction (NWP) skills, especially high-impact weather (HIW) events.","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":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10794787","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142858940","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-12DOI: 10.1109/LGRS.2024.3516078
Jiajun Yang;Wenjing Wang;Keyan Chen;Liqin Liu;Zhengxia Zou;Zhenwei Shi
Optical remote sensing imagery is often compromised by cloud cover, making effective cloud-removal techniques essential for enhancing the usability of such data. We designed a novel structural representation-guided generative adversarial network (GAN) framework for cloud removal, in which structure and gradient branches are integrated into the network, helping the model focus on the structural representations of ground objects during image reconstruction. Different from previous methods that concentrate on recovering pixel information, we emphasize learning the structural information of remote sensing images. We then utilize error feedback to fuse features from the structural auxiliary branch, guiding the image reconstruction process. During the training phase, synthetic cloud images are used to supervise the optimization of the cloud-removal network, while real cloud images are employed in an adversarial training manner for unsupervised learning to improve the generalization ability of the network. Additionally, multitemporal revisit images from remote sensing satellites are employed as auxiliary inputs, aiding the network to remove thick clouds reliably. We evaluated our framework on a dataset derived from SEN12MS-CR, and the proposed method outperformed classical cloud-removal methods in both objective performance and subjective visual quality. Furthermore, compared to other methods, our approach achieved superior cloud-removal results on real images.
{"title":"Structural Representation-Guided GAN for Remote Sensing Image Cloud Removal","authors":"Jiajun Yang;Wenjing Wang;Keyan Chen;Liqin Liu;Zhengxia Zou;Zhenwei Shi","doi":"10.1109/LGRS.2024.3516078","DOIUrl":"https://doi.org/10.1109/LGRS.2024.3516078","url":null,"abstract":"Optical remote sensing imagery is often compromised by cloud cover, making effective cloud-removal techniques essential for enhancing the usability of such data. We designed a novel structural representation-guided generative adversarial network (GAN) framework for cloud removal, in which structure and gradient branches are integrated into the network, helping the model focus on the structural representations of ground objects during image reconstruction. Different from previous methods that concentrate on recovering pixel information, we emphasize learning the structural information of remote sensing images. We then utilize error feedback to fuse features from the structural auxiliary branch, guiding the image reconstruction process. During the training phase, synthetic cloud images are used to supervise the optimization of the cloud-removal network, while real cloud images are employed in an adversarial training manner for unsupervised learning to improve the generalization ability of the network. Additionally, multitemporal revisit images from remote sensing satellites are employed as auxiliary inputs, aiding the network to remove thick clouds reliably. We evaluated our framework on a dataset derived from SEN12MS-CR, and the proposed method outperformed classical cloud-removal methods in both objective performance and subjective visual quality. Furthermore, compared to other methods, our approach achieved superior cloud-removal results on real images.","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":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142858946","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}
Pub Date : 2024-12-12DOI: 10.1109/LGRS.2024.3516474
Ge Jiang;Derek D. Lichti;Tiangang Yin;Wai Yeung Yan
The demand for accurate airborne LiDAR point cloud classification has increased with improved resolutions of land cover map products. Although existing deep learning-based methods are capable of classifying airborne LiDAR point clouds, these methods indeed have a limited capability to extract the local features and suffer from global and local information losses with the commonly used pooling approaches. Therefore, we present a deep learning-based optimal homogeneous neighbor selection (HNS) and hierarchical pooling by exploiting maximum entropy, called MEHPool. The module is designed to directly extract sufficient homogeneous neighbor points for each point, followed by a designed graph pooling (GP) layer that encapsulates the selected homogeneous neighbor points into small-size graphs to build hierarchical features. The plug-and-play module consisting of an HNS module, two GP layers, and three graph neural networks (GNNs) can be easily embedded into various networks for point cloud classification and produces the architecture MEHPool-Net in this letter. Our experimental results show that the proposed MEHPool-Net realizes effective performance for multispectral airborne LiDAR point cloud classification, consistently outperforms four other deep learning methods, and confirms the superiority of the GP module compared with five other pooling methods.
{"title":"Multispectral Airborne LiDAR Point Cloud Classification With Maximum Entropy Hierarchical Pooling","authors":"Ge Jiang;Derek D. Lichti;Tiangang Yin;Wai Yeung Yan","doi":"10.1109/LGRS.2024.3516474","DOIUrl":"https://doi.org/10.1109/LGRS.2024.3516474","url":null,"abstract":"The demand for accurate airborne LiDAR point cloud classification has increased with improved resolutions of land cover map products. Although existing deep learning-based methods are capable of classifying airborne LiDAR point clouds, these methods indeed have a limited capability to extract the local features and suffer from global and local information losses with the commonly used pooling approaches. Therefore, we present a deep learning-based optimal homogeneous neighbor selection (HNS) and hierarchical pooling by exploiting maximum entropy, called MEHPool. The module is designed to directly extract sufficient homogeneous neighbor points for each point, followed by a designed graph pooling (GP) layer that encapsulates the selected homogeneous neighbor points into small-size graphs to build hierarchical features. The plug-and-play module consisting of an HNS module, two GP layers, and three graph neural networks (GNNs) can be easily embedded into various networks for point cloud classification and produces the architecture MEHPool-Net in this letter. Our experimental results show that the proposed MEHPool-Net realizes effective performance for multispectral airborne LiDAR point cloud classification, consistently outperforms four other deep learning methods, and confirms the superiority of the GP module compared with five other pooling methods.","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":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142870220","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}
Pub Date : 2024-12-11DOI: 10.1109/LGRS.2024.3510579
Fawei Miao;Yan-Xiao He;Jingyang Ni;Sanyi Yuan;Shangxu Wang
One of the leading challenges in hydrocarbon recovery is predicting fluid distribution throughout the reservoir, using dispersion of seismic elastic parameters to solve this problem is a method with great potential. Previous studies reveal that predicting frequency-dependent seismic elastic parameters is difficult because of their sensitivity to seismic wave amplitude. The frequency-dependent AVO inversion schemes are widely used to estimate the dispersion gradient attributes for fluid prediction. However, these methods strongly depend on the advanced spectral decomposition and the wavelet overprint effect in time-frequency information. For this reason, this study presents an innovative technique that combines prestack AVO inversion and linear Bayesian inversion algorithm to predict directly frequency-dependent P-wave velocity of multilayered medium from seismic reflection data, which can quantitatively describe the change of P-wave velocity in seismic frequency band. Furthermore, frequency-dependent elastic parameters were used to define a dispersion factor for fluid prediction in thin sandstone reservoirs. The novelty of the study is that the proposed approach introduces prestack AVO inversion to provide reliable initial model and constructs dispersive P-wave velocity inversion framework of layered medium for the first time. Additionally, the dispersive elastic parameters have more potential applications than the dispersion gradient attributes. Tests on the synthetic and real data demonstrate that the frequency-dependent P-wave velocity of multilayered medium can be estimated reasonably and stably. In this application, we use a test well to assess locally the performance of the technique.
{"title":"A Novel Approach of Frequency-Dependent Seismic Elastic Parameters Inversion for Fluid Prediction at Thin Sandstone Reservoirs","authors":"Fawei Miao;Yan-Xiao He;Jingyang Ni;Sanyi Yuan;Shangxu Wang","doi":"10.1109/LGRS.2024.3510579","DOIUrl":"https://doi.org/10.1109/LGRS.2024.3510579","url":null,"abstract":"One of the leading challenges in hydrocarbon recovery is predicting fluid distribution throughout the reservoir, using dispersion of seismic elastic parameters to solve this problem is a method with great potential. Previous studies reveal that predicting frequency-dependent seismic elastic parameters is difficult because of their sensitivity to seismic wave amplitude. The frequency-dependent AVO inversion schemes are widely used to estimate the dispersion gradient attributes for fluid prediction. However, these methods strongly depend on the advanced spectral decomposition and the wavelet overprint effect in time-frequency information. For this reason, this study presents an innovative technique that combines prestack AVO inversion and linear Bayesian inversion algorithm to predict directly frequency-dependent P-wave velocity of multilayered medium from seismic reflection data, which can quantitatively describe the change of P-wave velocity in seismic frequency band. Furthermore, frequency-dependent elastic parameters were used to define a dispersion factor for fluid prediction in thin sandstone reservoirs. The novelty of the study is that the proposed approach introduces prestack AVO inversion to provide reliable initial model and constructs dispersive P-wave velocity inversion framework of layered medium for the first time. Additionally, the dispersive elastic parameters have more potential applications than the dispersion gradient attributes. Tests on the synthetic and real data demonstrate that the frequency-dependent P-wave velocity of multilayered medium can be estimated reasonably and stably. In this application, we use a test well to assess locally the performance of the technique.","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":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142870212","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}
Pub Date : 2024-12-11DOI: 10.1109/LGRS.2024.3514913
Jingyan Yu;Yunlong Zhu;Zhixin Deng;Yanling Zhao
The global navigation satellite system (GNSS) reflectometry synthetic aperture radar (SAR) interferometry (GNSS-R InSAR) system enables elevation deformation retrieval using a single satellite. However, variations in bistatic configurations and the generally low accuracy of most satellites necessitate a refined satellite selection method. Thus, this letter proposes a satellite selection algorithm for GNSS-R InSAR, aiming to optimize satellite selection and data acquisition time to improve the precision of elevation deformation monitoring. First, the interferometric phase model based on the repeat-pass concept was established using GPS L5 signals. Second, a satellite selection algorithm was proposed that incorporates constraints on resolution cells, spatial baseline, and phase sensitivity for elevation deformation, derived from an analysis of the repeat-pass spatial baseline of GNSS satellites, interferometric phase sensitivity, and the maximum deformation range. Third, 24 sets of repeat-pass data were collected, and the experimental results validate the effectiveness of this single-satellite selection approach.
{"title":"A Satellite Selection Algorithm for GNSS-R InSAR Elevation Deformation Retrieval","authors":"Jingyan Yu;Yunlong Zhu;Zhixin Deng;Yanling Zhao","doi":"10.1109/LGRS.2024.3514913","DOIUrl":"https://doi.org/10.1109/LGRS.2024.3514913","url":null,"abstract":"The global navigation satellite system (GNSS) reflectometry synthetic aperture radar (SAR) interferometry (GNSS-R InSAR) system enables elevation deformation retrieval using a single satellite. However, variations in bistatic configurations and the generally low accuracy of most satellites necessitate a refined satellite selection method. Thus, this letter proposes a satellite selection algorithm for GNSS-R InSAR, aiming to optimize satellite selection and data acquisition time to improve the precision of elevation deformation monitoring. First, the interferometric phase model based on the repeat-pass concept was established using GPS L5 signals. Second, a satellite selection algorithm was proposed that incorporates constraints on resolution cells, spatial baseline, and phase sensitivity for elevation deformation, derived from an analysis of the repeat-pass spatial baseline of GNSS satellites, interferometric phase sensitivity, and the maximum deformation range. Third, 24 sets of repeat-pass data were collected, and the experimental results validate the effectiveness of this single-satellite selection approach.","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":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142844386","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}
Pub Date : 2024-12-11DOI: 10.1109/LGRS.2024.3515207
Jingwei Deng;Xiaolin Han;Huan Zhang;Weidong Sun
Several spectral and spatial dictionary-based methods exist for fusing a high-spatial-resolution multispectral image (HR-MSI) with a low-spatial-resolution hyperspectral image (LR-HSI). However, using only one type of dictionary is insufficient to preserve spatial and spectral information simultaneously, while utilizing both dictionaries would increase the computational costs. To address this problem, we propose a fast fusion method (called FFD) for HR-MSIs and LR-HSIs via subpixel-shift decomposition. In this method, through joint optimization of low rank and sparsity within the framework of subpixel shift and sparse representation, an ultimate spectral dictionary is acquired along with its associated coefficients. Specifically, the HR-MSI is decomposed into subimage sequences of the same spatial resolution as the LR-HSI first, to replace the use of spatial dictionaries. Subsequently, a new fusion model is constructed based on this decomposition incorporating the constraints of low rank and sparsity, and especially, a low-rank term is introduced to constrain the spectral consistency along the decomposition direction. Then, the model is theoretically derived by using the alternating direction method of multipliers (ADMM) method, and a joint optimization for the spectral dictionary and its sparse coefficients is obtained. Finally, the desired HR-HSI can be reconstructed by using the above fused subimages through a simple inversed composition. Experimental results on different datasets show that compared with the other related methods, our proposed FFD can achieve an equivalent fusion effect to the best of them in a much shorter time.
{"title":"A Fast Fusion Method for Multi- and Hyperspectral Images via Subpixel-Shift Decomposition","authors":"Jingwei Deng;Xiaolin Han;Huan Zhang;Weidong Sun","doi":"10.1109/LGRS.2024.3515207","DOIUrl":"https://doi.org/10.1109/LGRS.2024.3515207","url":null,"abstract":"Several spectral and spatial dictionary-based methods exist for fusing a high-spatial-resolution multispectral image (HR-MSI) with a low-spatial-resolution hyperspectral image (LR-HSI). However, using only one type of dictionary is insufficient to preserve spatial and spectral information simultaneously, while utilizing both dictionaries would increase the computational costs. To address this problem, we propose a fast fusion method (called FFD) for HR-MSIs and LR-HSIs via subpixel-shift decomposition. In this method, through joint optimization of low rank and sparsity within the framework of subpixel shift and sparse representation, an ultimate spectral dictionary is acquired along with its associated coefficients. Specifically, the HR-MSI is decomposed into subimage sequences of the same spatial resolution as the LR-HSI first, to replace the use of spatial dictionaries. Subsequently, a new fusion model is constructed based on this decomposition incorporating the constraints of low rank and sparsity, and especially, a low-rank term is introduced to constrain the spectral consistency along the decomposition direction. Then, the model is theoretically derived by using the alternating direction method of multipliers (ADMM) method, and a joint optimization for the spectral dictionary and its sparse coefficients is obtained. Finally, the desired HR-HSI can be reconstructed by using the above fused subimages through a simple inversed composition. Experimental results on different datasets show that compared with the other related methods, our proposed FFD can achieve an equivalent fusion effect to the best of them in a much shorter time.","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":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142858939","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}
Pub Date : 2024-12-11DOI: 10.1109/LGRS.2024.3515136
Jingliang Guo;Mengke Yuan;Tong Wang;Zhifeng Li;Xiaohong Jia;Dong-Ming Yan
Reference-based remote sensing super-resolution (RefRS-SR) method shows great potential for improving both spatial resolution and coverage area of remote sensing images, by which high-resolution (HR) reference images can supplement fine details for low-resolution (LR) but wide coverage images. However, most RefRS-SR methods treat the reference as a static template and unidirectionally transfer the high-frequency information to the LR input. To address the issue of inefficient and inaccurate guided super-resolving, we propose a new RefRS-SR method with dynamic reference textures exploitation dubbed DTESR. The key referenced restoration (Ref Restoration) module consists of three components: correlation generation, texture enhancement and refinement (TER), and adaptive similarity-based fusion to progressively reconstruct high correlation and delicate textures for the LR input. Specifically, both the LR input and reference features are utilized for precise correlation generation. Next, both features are enhanced and refined with the most suitable reference under the guidance of the correlation map. Moreover, a learnable fusion method is designed to maintain the consistency of adjacent pixels. These operations will be iteratively applied to the three reconstruction scales to promote the exploitation of the Ref features. Through comprehensive quantitative and qualitative evaluations, our experimental results demonstrate that DTESR surpasses the current state-of-the-art RefRS-SR methods.
{"title":"DTESR: Remote Sensing Imagery Super-Resolution With Dynamic Reference Textures Exploitation","authors":"Jingliang Guo;Mengke Yuan;Tong Wang;Zhifeng Li;Xiaohong Jia;Dong-Ming Yan","doi":"10.1109/LGRS.2024.3515136","DOIUrl":"https://doi.org/10.1109/LGRS.2024.3515136","url":null,"abstract":"Reference-based remote sensing super-resolution (RefRS-SR) method shows great potential for improving both spatial resolution and coverage area of remote sensing images, by which high-resolution (HR) reference images can supplement fine details for low-resolution (LR) but wide coverage images. However, most RefRS-SR methods treat the reference as a static template and unidirectionally transfer the high-frequency information to the LR input. To address the issue of inefficient and inaccurate guided super-resolving, we propose a new RefRS-SR method with dynamic reference textures exploitation dubbed DTESR. The key referenced restoration (Ref Restoration) module consists of three components: correlation generation, texture enhancement and refinement (TER), and adaptive similarity-based fusion to progressively reconstruct high correlation and delicate textures for the LR input. Specifically, both the LR input and reference features are utilized for precise correlation generation. Next, both features are enhanced and refined with the most suitable reference under the guidance of the correlation map. Moreover, a learnable fusion method is designed to maintain the consistency of adjacent pixels. These operations will be iteratively applied to the three reconstruction scales to promote the exploitation of the Ref features. Through comprehensive quantitative and qualitative evaluations, our experimental results demonstrate that DTESR surpasses the current state-of-the-art RefRS-SR methods.","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":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142858947","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}
Pub Date : 2024-12-11DOI: 10.1109/LGRS.2024.3514857
Hongxin Yang;Shanshan Xu;Sheng Xu
Building edge points, as essential geometric features, are crucial for advancing smart city initiatives and ensuring the precise reconstruction of 3-D structures. However, existing methods struggle to effectively design point-to-edge distance constraints for accurate building edge point identification. In this letter, we propose a novel self-supervised learning (SSL)-based pretraining framework that integrates an innovative edge point identification loss function for extracting building edge points. Specifically, we use an SSL-based feature extractor, leveraging a masked autoencoder to generate pointwise features from the input building point clouds. These features are subsequently processed by the proposed edge point identification module, which optimizes three key distance-based loss functions: the distance between any input point and its nearest edge, the distance between candidate edge points and the projection of the input point, and the distance between candidate edge points and the edges themselves. The proposed framework demonstrates superior performance in edge point extraction across both partial and complete datasets, outperforming existing methods in edge point identification.
{"title":"A Self-Supervised Pretraining Framework for Context-Aware Building Edge Extraction From 3-D Point Clouds","authors":"Hongxin Yang;Shanshan Xu;Sheng Xu","doi":"10.1109/LGRS.2024.3514857","DOIUrl":"https://doi.org/10.1109/LGRS.2024.3514857","url":null,"abstract":"Building edge points, as essential geometric features, are crucial for advancing smart city initiatives and ensuring the precise reconstruction of 3-D structures. However, existing methods struggle to effectively design point-to-edge distance constraints for accurate building edge point identification. In this letter, we propose a novel self-supervised learning (SSL)-based pretraining framework that integrates an innovative edge point identification loss function for extracting building edge points. Specifically, we use an SSL-based feature extractor, leveraging a masked autoencoder to generate pointwise features from the input building point clouds. These features are subsequently processed by the proposed edge point identification module, which optimizes three key distance-based loss functions: the distance between any input point and its nearest edge, the distance between candidate edge points and the projection of the input point, and the distance between candidate edge points and the edges themselves. The proposed framework demonstrates superior performance in edge point extraction across both partial and complete datasets, outperforming existing methods in edge point identification.","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":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142858949","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}
Pub Date : 2024-12-11DOI: 10.1109/LGRS.2024.3515956
Junkai Ge;Huaifeng Sun;Rui Liu;Bo Tian;Ziqiang Zheng;Yongqiang Li
In the ground-penetrating radar (GPR) detection of concrete structures, the reflection of rebar layers often obscures the useful signals below. In this letter, an effective and practical method for removing rebar clutter is proposed. It is based on iterative F-k migration and demigration, combined with real-time mask window and some classic GPR data processing steps. First, we calculate the wave velocity through travel time and layer thickness and migrate the B-scan data. Then, we create a mask window to extract the focused rebar reflection. Finally, the rebar clutter is restored through F-k demigration and removed from the original data. Meanwhile, multiple iterations are performed to ensure the complete removal of rebar clutter. The proposed method is not limited by data size and observation scale. The effectiveness of the proposed method is demonstrated by both numerical simulations and model field experiments.
{"title":"Removing Rebar Clutter Through Iterative F-k Migration in GPR Data","authors":"Junkai Ge;Huaifeng Sun;Rui Liu;Bo Tian;Ziqiang Zheng;Yongqiang Li","doi":"10.1109/LGRS.2024.3515956","DOIUrl":"https://doi.org/10.1109/LGRS.2024.3515956","url":null,"abstract":"In the ground-penetrating radar (GPR) detection of concrete structures, the reflection of rebar layers often obscures the useful signals below. In this letter, an effective and practical method for removing rebar clutter is proposed. It is based on iterative F-k migration and demigration, combined with real-time mask window and some classic GPR data processing steps. First, we calculate the wave velocity through travel time and layer thickness and migrate the B-scan data. Then, we create a mask window to extract the focused rebar reflection. Finally, the rebar clutter is restored through F-k demigration and removed from the original data. Meanwhile, multiple iterations are performed to ensure the complete removal of rebar clutter. The proposed method is not limited by data size and observation scale. The effectiveness of the proposed method is demonstrated by both numerical simulations and model field experiments.","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":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142870199","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}
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