Improvement of the feature tracking and patter matching algorithm for sea ice motion retrieval from SAR and optical imagery
This study presents an improved, versatile, and efficient algorithm based on the Oriented FAST and Rotated BRIEF (ORB) combined with the maximum cross-correlation (MCC) (ORB-MCC) for extracting sea ice motion (SIM) vectors. Quadtree ORB (Q-ORB) extracts more uniform feature points than ORB (uniformi...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-08-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1569843222001108 |
| _version_ | 1818516482583166976 |
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| author | Mingci Li Chunxia Zhou Xiaoli Chen Yong Liu Bing Li Tingting Liu |
| author_facet | Mingci Li Chunxia Zhou Xiaoli Chen Yong Liu Bing Li Tingting Liu |
| author_sort | Mingci Li |
| collection | DOAJ |
| description | This study presents an improved, versatile, and efficient algorithm based on the Oriented FAST and Rotated BRIEF (ORB) combined with the maximum cross-correlation (MCC) (ORB-MCC) for extracting sea ice motion (SIM) vectors. Quadtree ORB (Q-ORB) extracts more uniform feature points than ORB (uniformity is 3 times higher) and eliminates the concentration of ORB-extracted feature points on ice ridges, leads and coastlines, thereby providing excellent initial conditions for MCC calculations. In addition, a geographic grid-based matching (GGM) algorithm is developed to replace the brute-force matching algorithm (BFM). GGM is 8–10 times more efficient for matching feature points than BFM, thereby increasing the computational efficiency of extracting SIM vectors. Moreover, a locally consistent (LC) flow field filtering process is incorporated to facilitate the filtering of the SIM field. Combining cross-correlation-coefficient-threshold (CCCT)-based and LC filtering processes eliminates erroneous vectors more efficiently than using a CCCT-based filtering process alone. The improved algorithm, named Q-ORB-MCC, is used to extract SIM vectors from imagery acquired by the Sentinel-1 Synthetic-Aperture Radar (SAR), Envisat Advanced SAR (ASAR), Phased Array type L-band SAR-2 (PALSAR-2) onboard the Advanced Land Observing Satellite-2 (ALOS-2), and Moderate Resolution Imaging Spectroradiometer (MODIS). An analysis of the accuracy and effectiveness of the extracted SIM vectors shows that Q-ORB-MCC extracted SIM vectors from Sentinel-1, ASAR, and MODIS images with 4%, 253%, and 62% higher accuracy than ORB-MCC, respectively. Meanwhile Q-ORB-MCC could obtain more SIM vectors from Sentinel-1 and ASAR images. |
| first_indexed | 2024-12-11T00:42:52Z |
| format | Article |
| id | doaj.art-270d6e12279c423186491e0465e3f233 |
| institution | Directory Open Access Journal |
| issn | 1569-8432 |
| language | English |
| last_indexed | 2024-12-11T00:42:52Z |
| publishDate | 2022-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj.art-270d6e12279c423186491e0465e3f2332022-12-22T01:26:52ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322022-08-01112102908Improvement of the feature tracking and patter matching algorithm for sea ice motion retrieval from SAR and optical imageryMingci Li0Chunxia Zhou1Xiaoli Chen2Yong Liu3Bing Li4Tingting Liu5Chinese Antarctic Center of Surveying and Mapping, Wuhan University, Wuhan, ChinaChinese Antarctic Center of Surveying and Mapping, Wuhan University, Wuhan, China; Corresponding author.Chinese Antarctic Center of Surveying and Mapping, Wuhan University, Wuhan, ChinaChinese Antarctic Center of Surveying and Mapping, Wuhan University, Wuhan, ChinaSchool of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan, ChinaChinese Antarctic Center of Surveying and Mapping, Wuhan University, Wuhan, ChinaThis study presents an improved, versatile, and efficient algorithm based on the Oriented FAST and Rotated BRIEF (ORB) combined with the maximum cross-correlation (MCC) (ORB-MCC) for extracting sea ice motion (SIM) vectors. Quadtree ORB (Q-ORB) extracts more uniform feature points than ORB (uniformity is 3 times higher) and eliminates the concentration of ORB-extracted feature points on ice ridges, leads and coastlines, thereby providing excellent initial conditions for MCC calculations. In addition, a geographic grid-based matching (GGM) algorithm is developed to replace the brute-force matching algorithm (BFM). GGM is 8–10 times more efficient for matching feature points than BFM, thereby increasing the computational efficiency of extracting SIM vectors. Moreover, a locally consistent (LC) flow field filtering process is incorporated to facilitate the filtering of the SIM field. Combining cross-correlation-coefficient-threshold (CCCT)-based and LC filtering processes eliminates erroneous vectors more efficiently than using a CCCT-based filtering process alone. The improved algorithm, named Q-ORB-MCC, is used to extract SIM vectors from imagery acquired by the Sentinel-1 Synthetic-Aperture Radar (SAR), Envisat Advanced SAR (ASAR), Phased Array type L-band SAR-2 (PALSAR-2) onboard the Advanced Land Observing Satellite-2 (ALOS-2), and Moderate Resolution Imaging Spectroradiometer (MODIS). An analysis of the accuracy and effectiveness of the extracted SIM vectors shows that Q-ORB-MCC extracted SIM vectors from Sentinel-1, ASAR, and MODIS images with 4%, 253%, and 62% higher accuracy than ORB-MCC, respectively. Meanwhile Q-ORB-MCC could obtain more SIM vectors from Sentinel-1 and ASAR images.http://www.sciencedirect.com/science/article/pii/S1569843222001108Sea ice motionFeature trackingMaximum cross-correlationQ-ORBGeographic grid-based matchingLocally consistent filtering |
| spellingShingle | Mingci Li Chunxia Zhou Xiaoli Chen Yong Liu Bing Li Tingting Liu Improvement of the feature tracking and patter matching algorithm for sea ice motion retrieval from SAR and optical imagery International Journal of Applied Earth Observations and Geoinformation Sea ice motion Feature tracking Maximum cross-correlation Q-ORB Geographic grid-based matching Locally consistent filtering |
| title | Improvement of the feature tracking and patter matching algorithm for sea ice motion retrieval from SAR and optical imagery |
| title_full | Improvement of the feature tracking and patter matching algorithm for sea ice motion retrieval from SAR and optical imagery |
| title_fullStr | Improvement of the feature tracking and patter matching algorithm for sea ice motion retrieval from SAR and optical imagery |
| title_full_unstemmed | Improvement of the feature tracking and patter matching algorithm for sea ice motion retrieval from SAR and optical imagery |
| title_short | Improvement of the feature tracking and patter matching algorithm for sea ice motion retrieval from SAR and optical imagery |
| title_sort | improvement of the feature tracking and patter matching algorithm for sea ice motion retrieval from sar and optical imagery |
| topic | Sea ice motion Feature tracking Maximum cross-correlation Q-ORB Geographic grid-based matching Locally consistent filtering |
| url | http://www.sciencedirect.com/science/article/pii/S1569843222001108 |
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