Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Faculty of Construction and Environment | en_US |
| dc.contributor | Department of Land Surveying and Geo-Informatics | en_US |
| dc.contributor.advisor | Wu, Bo (LSGI) | - |
| dc.creator | Zeng, Hai | - |
| dc.identifier.uri | https://theses.lib.polyu.edu.hk/handle/200/10529 | - |
| dc.language | English | en_US |
| dc.publisher | Hong Kong Polytechnic University | - |
| dc.rights | All rights reserved | en_US |
| dc.title | Improved SIFT algorithm for lunar remote sensing image matching with large illumination difference | en_US |
| dcterms.abstract | This research presents an improved algorithm for Scale Invariant Feature Transform (SIFT) to perform reliable and robust matching for lunar remote sensing images with large illumination difference. The texture features of lunar remote sensing images are mainly dependent on the illumination. The improved SIFT algorithm, therefore, first introduces an approach to estimate the illumination orientation of images from histogram statistics for keypoint orientations. Then both the illumination orientation and computed orientation histogram are used to create a weight function that is used to add to gradient magnitude of each sample in keypoint neighbor when calculating the improved orientation of keypoints. Experiments demonstrated the improved approach is effective to reduce and eliminate illumination effects for keypoints. The same weight function is employed when computing the 128-Dimensioanl descriptor for keypoints. Based on the improved SIFT descriptors, this research also developed an improved matching method by combining the nearest Euclidean distance matching with the template matching and RANSAC filter. Experimental results showed that the improved SIFT algorithm can efficiently and reliably acquires approximate 40% more correct matches than using the original SIFT algorithm. By statistics, the improved algorithm is well performed to adapt for illumination changes even in 180 degrees, as more than 60% of newly obtained matching pairs are associated to (reverse) illumination orientation. At meantime, there are a lot of new matches coming from those regions lacking of texture where original SIFT algorithm cannot successful match them. The improved SIFT algorithm is, therefore, useful in lunar image matching for those textureless regions as well. | en_US |
| dcterms.extent | x, 101 pages : color illustrations | en_US |
| dcterms.isPartOf | PolyU Electronic Theses | en_US |
| dcterms.issued | 2017 | en_US |
| dcterms.educationalLevel | M.Sc. | en_US |
| dcterms.educationalLevel | All Master | en_US |
| dcterms.LCSH | Remote sensing -- Mathematics | en_US |
| dcterms.LCSH | Image processing -- Digital techniques | en_US |
| dcterms.LCSH | Hong Kong Polytechnic University -- Dissertations | en_US |
| dcterms.accessRights | restricted access | en_US |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 991022385850303411.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 7.88 MB | Adobe PDF | View/Open |
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