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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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | European Journal of Remote Sensing |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/22797254.2023.2293163 |
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| _version_ | 1850062021964333056 |
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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. |
| format | Article |
| id | doaj-art-329c284b0c3f485ba03cc2cfdcce4f68 |
| institution | DOAJ |
| issn | 2279-7254 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | European Journal of Remote Sensing |
| 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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