Inclined Aerial Image and Satellite Image Matching Based on Edge Curve Direction Angle Features
Optical remote sensing images are easily affected by atmospheric absorption and scattering, and the low contrast and low signal-to-noise ratio (SNR) of aerial images as well as the different sensors of aerial and satellite images bring a great challenge to image matching. A tilted aerial image and s...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/2/268 |
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| author | Hao Wang Chongyang Liu Yalin Ding Chao Sun Guoqin Yuan Hongwen Zhang |
| author_facet | Hao Wang Chongyang Liu Yalin Ding Chao Sun Guoqin Yuan Hongwen Zhang |
| author_sort | Hao Wang |
| collection | DOAJ |
| description | Optical remote sensing images are easily affected by atmospheric absorption and scattering, and the low contrast and low signal-to-noise ratio (SNR) of aerial images as well as the different sensors of aerial and satellite images bring a great challenge to image matching. A tilted aerial image and satellite image matching algorithm based on edge curve direction angle features (ECDAF) is proposed, which accomplishes image matching by extracting the edge features of the images and establishing the curve direction angle feature descriptors. First, tilt and resolution transforms are performed on the satellite image, and edge detection and contour extraction are performed on the aerial image and transformed satellite image to make preparations for image matching. Then, corner points are detected and feature descriptors are constructed based on the edge curve direction angle. Finally, the integrated matching similarity is computed to realize aerial–satellite image matching. Experiments run on a variety of remote sensing datasets including forests, hills, farmland, and lake scenes demonstrate that the effectiveness of the proposed algorithm shows a great improvement over existing state-of-the-art algorithms. |
| format | Article |
| id | doaj-art-228da7bd9b414668bd25ce65f2e2685f |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-228da7bd9b414668bd25ce65f2e2685f2025-01-24T13:47:56ZengMDPI AGRemote Sensing2072-42922025-01-0117226810.3390/rs17020268Inclined Aerial Image and Satellite Image Matching Based on Edge Curve Direction Angle FeaturesHao Wang0Chongyang Liu1Yalin Ding2Chao Sun3Guoqin Yuan4Hongwen Zhang5Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaChangchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, ChinaOptical remote sensing images are easily affected by atmospheric absorption and scattering, and the low contrast and low signal-to-noise ratio (SNR) of aerial images as well as the different sensors of aerial and satellite images bring a great challenge to image matching. A tilted aerial image and satellite image matching algorithm based on edge curve direction angle features (ECDAF) is proposed, which accomplishes image matching by extracting the edge features of the images and establishing the curve direction angle feature descriptors. First, tilt and resolution transforms are performed on the satellite image, and edge detection and contour extraction are performed on the aerial image and transformed satellite image to make preparations for image matching. Then, corner points are detected and feature descriptors are constructed based on the edge curve direction angle. Finally, the integrated matching similarity is computed to realize aerial–satellite image matching. Experiments run on a variety of remote sensing datasets including forests, hills, farmland, and lake scenes demonstrate that the effectiveness of the proposed algorithm shows a great improvement over existing state-of-the-art algorithms.https://www.mdpi.com/2072-4292/17/2/268image matchingcurve matchingfeature extractionedge curve direction angleremote sensing |
| spellingShingle | Hao Wang Chongyang Liu Yalin Ding Chao Sun Guoqin Yuan Hongwen Zhang Inclined Aerial Image and Satellite Image Matching Based on Edge Curve Direction Angle Features Remote Sensing image matching curve matching feature extraction edge curve direction angle remote sensing |
| title | Inclined Aerial Image and Satellite Image Matching Based on Edge Curve Direction Angle Features |
| title_full | Inclined Aerial Image and Satellite Image Matching Based on Edge Curve Direction Angle Features |
| title_fullStr | Inclined Aerial Image and Satellite Image Matching Based on Edge Curve Direction Angle Features |
| title_full_unstemmed | Inclined Aerial Image and Satellite Image Matching Based on Edge Curve Direction Angle Features |
| title_short | Inclined Aerial Image and Satellite Image Matching Based on Edge Curve Direction Angle Features |
| title_sort | inclined aerial image and satellite image matching based on edge curve direction angle features |
| topic | image matching curve matching feature extraction edge curve direction angle remote sensing |
| url | https://www.mdpi.com/2072-4292/17/2/268 |
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