SNLRUX++ for Building Extraction From High-Resolution Remote Sensing Images
Building extraction plays an important role in high-resolution remote sensing image processing, which can be used as the basis for urban planning and demographic analysis. In recent years, many powerful general semantic segmentation models have emerged, but these models often perform poorly when tra...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9652051/ |
| _version_ | 1828922884402184192 |
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| author | Yanjing Lei Jiamin Yu Sixian Chan Wei Wu Xiaoying Liu |
| author_facet | Yanjing Lei Jiamin Yu Sixian Chan Wei Wu Xiaoying Liu |
| author_sort | Yanjing Lei |
| collection | DOAJ |
| description | Building extraction plays an important role in high-resolution remote sensing image processing, which can be used as the basis for urban planning and demographic analysis. In recent years, many powerful general semantic segmentation models have emerged, but these models often perform poorly when transferred to remote sensing images because of the characteristics of remote sensing images. To this end, we propose a new deep learning network called Selective Nonlocal ResUNeXt++ (SNLRUX++) for building extraction. First, the cascaded multiscale feature fusion is proposed to transform the high-performance image classification network ResNeXt into the segmentation network ResUNeXt++. Second, selective nonlocal operation is designed to establish long-range dependencies while avoiding introducing excessive noise and computational effort. Finally, multiscale prediction is applied as deep supervision to accelerate training and convergence, and improves prediction performance of objects at different scales. The experimental results on two different remote sensing image datasets show the effectiveness and generalization ability of the proposed method. |
| first_indexed | 2024-12-13T22:29:25Z |
| format | Article |
| id | doaj.art-2cf172d2b0314701abd9c6be49f69e92 |
| institution | Directory Open Access Journal |
| issn | 2151-1535 |
| language | English |
| last_indexed | 2024-12-13T22:29:25Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj.art-2cf172d2b0314701abd9c6be49f69e922022-12-21T23:29:07ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing2151-15352022-01-011540942110.1109/JSTARS.2021.31357059652051SNLRUX++ for Building Extraction From High-Resolution Remote Sensing ImagesYanjing Lei0Jiamin Yu1https://orcid.org/0000-0001-8915-7204Sixian Chan2https://orcid.org/0000-0001-8916-1174Wei Wu3Xiaoying Liu4College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, ChinaCollege of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, ChinaCollege of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, ChinaCollege of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, ChinaCollege of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, ChinaBuilding extraction plays an important role in high-resolution remote sensing image processing, which can be used as the basis for urban planning and demographic analysis. In recent years, many powerful general semantic segmentation models have emerged, but these models often perform poorly when transferred to remote sensing images because of the characteristics of remote sensing images. To this end, we propose a new deep learning network called Selective Nonlocal ResUNeXt++ (SNLRUX++) for building extraction. First, the cascaded multiscale feature fusion is proposed to transform the high-performance image classification network ResNeXt into the segmentation network ResUNeXt++. Second, selective nonlocal operation is designed to establish long-range dependencies while avoiding introducing excessive noise and computational effort. Finally, multiscale prediction is applied as deep supervision to accelerate training and convergence, and improves prediction performance of objects at different scales. The experimental results on two different remote sensing image datasets show the effectiveness and generalization ability of the proposed method.https://ieeexplore.ieee.org/document/9652051/Building extractionconvolution neural networkdeep learninghigh-resolution imageremote sensing |
| spellingShingle | Yanjing Lei Jiamin Yu Sixian Chan Wei Wu Xiaoying Liu SNLRUX++ for Building Extraction From High-Resolution Remote Sensing Images IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Building extraction convolution neural network deep learning high-resolution image remote sensing |
| title | SNLRUX++ for Building Extraction From High-Resolution Remote Sensing Images |
| title_full | SNLRUX++ for Building Extraction From High-Resolution Remote Sensing Images |
| title_fullStr | SNLRUX++ for Building Extraction From High-Resolution Remote Sensing Images |
| title_full_unstemmed | SNLRUX++ for Building Extraction From High-Resolution Remote Sensing Images |
| title_short | SNLRUX++ for Building Extraction From High-Resolution Remote Sensing Images |
| title_sort | snlrux for building extraction from high resolution remote sensing images |
| topic | Building extraction convolution neural network deep learning high-resolution image remote sensing |
| url | https://ieeexplore.ieee.org/document/9652051/ |
| work_keys_str_mv | AT yanjinglei snlruxforbuildingextractionfromhighresolutionremotesensingimages AT jiaminyu snlruxforbuildingextractionfromhighresolutionremotesensingimages AT sixianchan snlruxforbuildingextractionfromhighresolutionremotesensingimages AT weiwu snlruxforbuildingextractionfromhighresolutionremotesensingimages AT xiaoyingliu snlruxforbuildingextractionfromhighresolutionremotesensingimages |