A review of research on remote sensing images shadow detection and application to building extraction

Buildings are one of the most important habitats for humans, and therefore, accurate identification and extraction of building information in remote sensing images are crucial. Buildings in remote sensing images vary in shape and color due to differences in sensor acquisition methods, geographical l...

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Main Authors: Xueyan Dong, Jiannong Cao, Weiheng Zhao
Format: Article
Language:English
Published: Taylor & Francis Group 2024-12-01
Series:European Journal of Remote Sensing
Subjects:
Online Access:https://www.tandfonline.com/doi/10.1080/22797254.2023.2293163
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author Xueyan Dong
Jiannong Cao
Weiheng Zhao
author_facet Xueyan Dong
Jiannong Cao
Weiheng Zhao
author_sort Xueyan Dong
collection DOAJ
description Buildings are one of the most important habitats for humans, and therefore, accurate identification and extraction of building information in remote sensing images are crucial. Buildings in remote sensing images vary in shape and color due to differences in sensor acquisition methods, geographical location, and other factors. However, they all share a common feature – the presence of shadows. Obtaining accurate data from building shadows can provide a wealth of reliable information for building research. Consequently, it is crucial to review various methods for extracting building shadows, especially deep learning-based methods, to illustrate shadow implementation scenarios in building research: 1) building detection in very high resolution remote sensing images (VHRRSI); 2) building detection in SAR; 3) building change detection; 4) building damage assessment; 5) building height estimation; 6) building shadow removal; 7) other methods (such as building shadow data enhancement, detection of building shadows in ghost images, and conservation of historic buildings). This study discusses the advantages and disadvantages of building shadow detection methods and provides an overview of the datasets and evaluation metrics commonly used in studies of building shadow applications. We hope that this study will serve as a valuable reference for researchers in the field of building shadow studies.
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spelling doaj-art-329c284b0c3f485ba03cc2cfdcce4f682025-08-20T02:50:02ZengTaylor & Francis GroupEuropean Journal of Remote Sensing2279-72542024-12-0157110.1080/22797254.2023.2293163A review of research on remote sensing images shadow detection and application to building extractionXueyan Dong0Jiannong Cao1Weiheng Zhao2School of Earth Science and Resources, Chang’an University, Xi’an, Shaanxi Province, ChinaMinistry of Natural Resources, Key Laboratory of Degraded and Unused Land Consolidation Engineering, Chang’an University, Xi’an, ChinaSchool of Earth Science and Resources, Chang’an University, Xi’an, Shaanxi Province, ChinaBuildings are one of the most important habitats for humans, and therefore, accurate identification and extraction of building information in remote sensing images are crucial. Buildings in remote sensing images vary in shape and color due to differences in sensor acquisition methods, geographical location, and other factors. However, they all share a common feature – the presence of shadows. Obtaining accurate data from building shadows can provide a wealth of reliable information for building research. Consequently, it is crucial to review various methods for extracting building shadows, especially deep learning-based methods, to illustrate shadow implementation scenarios in building research: 1) building detection in very high resolution remote sensing images (VHRRSI); 2) building detection in SAR; 3) building change detection; 4) building damage assessment; 5) building height estimation; 6) building shadow removal; 7) other methods (such as building shadow data enhancement, detection of building shadows in ghost images, and conservation of historic buildings). This study discusses the advantages and disadvantages of building shadow detection methods and provides an overview of the datasets and evaluation metrics commonly used in studies of building shadow applications. We hope that this study will serve as a valuable reference for researchers in the field of building shadow studies.https://www.tandfonline.com/doi/10.1080/22797254.2023.2293163Building researchshadow detectiondeep learningremote sensing
spellingShingle Xueyan Dong
Jiannong Cao
Weiheng Zhao
A review of research on remote sensing images shadow detection and application to building extraction
European Journal of Remote Sensing
Building research
shadow detection
deep learning
remote sensing
title A review of research on remote sensing images shadow detection and application to building extraction
title_full A review of research on remote sensing images shadow detection and application to building extraction
title_fullStr A review of research on remote sensing images shadow detection and application to building extraction
title_full_unstemmed A review of research on remote sensing images shadow detection and application to building extraction
title_short A review of research on remote sensing images shadow detection and application to building extraction
title_sort review of research on remote sensing images shadow detection and application to building extraction
topic Building research
shadow detection
deep learning
remote sensing
url https://www.tandfonline.com/doi/10.1080/22797254.2023.2293163
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