Review of Road Segmentation for SAR Images
Road segmentation for synthetic aperture radar (SAR) images is of great practical significance. With the rapid development and wide application of SAR imaging technology, this problem has attracted much attention. At present, there are numerous road segmentation methods. This paper analyzes and summ...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-03-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/13/5/1011 |
| _version_ | 1797412603984609280 |
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| author | Zengguo Sun Hui Geng Zheng Lu Rafał Scherer Marcin Woźniak |
| author_facet | Zengguo Sun Hui Geng Zheng Lu Rafał Scherer Marcin Woźniak |
| author_sort | Zengguo Sun |
| collection | DOAJ |
| description | Road segmentation for synthetic aperture radar (SAR) images is of great practical significance. With the rapid development and wide application of SAR imaging technology, this problem has attracted much attention. At present, there are numerous road segmentation methods. This paper analyzes and summarizes the road segmentation methods for SAR images over the years. Firstly, the traditional road segmentation algorithms are classified according to the degree of automation and the principle. Advantages and disadvantages are introduced successively for each traditional method. Then, the popular segmentation methods based on deep learning in recent years are systematically introduced. Finally, novel deep segmentation neural networks based on the capsule paradigm and the self-attention mechanism are forecasted as future research for SAR images. |
| first_indexed | 2024-03-09T05:04:32Z |
| format | Article |
| id | doaj.art-eb681f2d14134573afed7a353727ad1d |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T05:04:32Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-eb681f2d14134573afed7a353727ad1d2023-12-03T12:56:27ZengMDPI AGRemote Sensing2072-42922021-03-01135101110.3390/rs13051011Review of Road Segmentation for SAR ImagesZengguo Sun0Hui Geng1Zheng Lu2Rafał Scherer3Marcin Woźniak4Key Laboratory of Modern Teaching Technology, Ministry of Education, Xi’an 710062, ChinaSchool of Computer Science, Shaanxi Normal University, Xi’an 710119, ChinaInstitute of Remote Sensing Satellite, Beijing 100094, ChinaDepartment of Intelligent Computer Systems, Czestochowa University of Technology, Armii Krajowej 36, 42-200 Częstochowa, PolandFaculty of Applied Mathematics, Silesian University of Technology, Kaszubska 23, 44-100 Gliwice, PolandRoad segmentation for synthetic aperture radar (SAR) images is of great practical significance. With the rapid development and wide application of SAR imaging technology, this problem has attracted much attention. At present, there are numerous road segmentation methods. This paper analyzes and summarizes the road segmentation methods for SAR images over the years. Firstly, the traditional road segmentation algorithms are classified according to the degree of automation and the principle. Advantages and disadvantages are introduced successively for each traditional method. Then, the popular segmentation methods based on deep learning in recent years are systematically introduced. Finally, novel deep segmentation neural networks based on the capsule paradigm and the self-attention mechanism are forecasted as future research for SAR images.https://www.mdpi.com/2072-4292/13/5/1011synthetic aperture radar imagesroad segmentationdeep learningcapsule networkself-attention mechanism |
| spellingShingle | Zengguo Sun Hui Geng Zheng Lu Rafał Scherer Marcin Woźniak Review of Road Segmentation for SAR Images Remote Sensing synthetic aperture radar images road segmentation deep learning capsule network self-attention mechanism |
| title | Review of Road Segmentation for SAR Images |
| title_full | Review of Road Segmentation for SAR Images |
| title_fullStr | Review of Road Segmentation for SAR Images |
| title_full_unstemmed | Review of Road Segmentation for SAR Images |
| title_short | Review of Road Segmentation for SAR Images |
| title_sort | review of road segmentation for sar images |
| topic | synthetic aperture radar images road segmentation deep learning capsule network self-attention mechanism |
| url | https://www.mdpi.com/2072-4292/13/5/1011 |
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