Optimizing SIFT for Matching of Short Wave Infrared and Visible Wavelength Images
The scale invariant feature transform (SIFT) is a widely used interest operator for supporting tasks such as 3D matching, 3D scene reconstruction, panorama stitching, image registration and motion tracking. Although SIFT is reported to be robust to disparate radiometric and geometric conditions in v...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2013-04-01
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| Series: | Remote Sensing |
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| Online Access: | http://www.mdpi.com/2072-4292/5/5/2037 |
| _version_ | 1798030799464824832 |
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| author | Simon J. Buckley Aleksandra A. Sima |
| author_facet | Simon J. Buckley Aleksandra A. Sima |
| author_sort | Simon J. Buckley |
| collection | DOAJ |
| description | The scale invariant feature transform (SIFT) is a widely used interest operator for supporting tasks such as 3D matching, 3D scene reconstruction, panorama stitching, image registration and motion tracking. Although SIFT is reported to be robust to disparate radiometric and geometric conditions in visible light imagery, using the default input parameters does not yield satisfactory results when matching imagery acquired at non-overlapping wavelengths. In this paper, optimization of the SIFT parameters for matching multi-wavelength image sets is documented. In order to integrate hyperspectral panoramic images with reference imagery and 3D data, corresponding points were required between visible light and short wave infrared images, each acquired from a slightly different position and with different resolutions and geometric projections. The default SIFT parameters resulted in too few points being found, requiring the influence of five key parameters on the number of matched points to be explored using statistical techniques. Results are discussed for two geological datasets. Using the SIFT operator with optimized parameters and an additional outlier elimination method, allowed between four and 22 times more homologous points to be found with improved image point distributions, than using the default parameter values recommended in the literature. |
| first_indexed | 2024-04-11T19:47:10Z |
| format | Article |
| id | doaj.art-86c2ffbccaa2408891e51cd7cf27c282 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-04-11T19:47:10Z |
| publishDate | 2013-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-86c2ffbccaa2408891e51cd7cf27c2822022-12-22T04:06:27ZengMDPI AGRemote Sensing2072-42922013-04-01552037205610.3390/rs5052037Optimizing SIFT for Matching of Short Wave Infrared and Visible Wavelength ImagesSimon J. BuckleyAleksandra A. SimaThe scale invariant feature transform (SIFT) is a widely used interest operator for supporting tasks such as 3D matching, 3D scene reconstruction, panorama stitching, image registration and motion tracking. Although SIFT is reported to be robust to disparate radiometric and geometric conditions in visible light imagery, using the default input parameters does not yield satisfactory results when matching imagery acquired at non-overlapping wavelengths. In this paper, optimization of the SIFT parameters for matching multi-wavelength image sets is documented. In order to integrate hyperspectral panoramic images with reference imagery and 3D data, corresponding points were required between visible light and short wave infrared images, each acquired from a slightly different position and with different resolutions and geometric projections. The default SIFT parameters resulted in too few points being found, requiring the influence of five key parameters on the number of matched points to be explored using statistical techniques. Results are discussed for two geological datasets. Using the SIFT operator with optimized parameters and an additional outlier elimination method, allowed between four and 22 times more homologous points to be found with improved image point distributions, than using the default parameter values recommended in the literature.http://www.mdpi.com/2072-4292/5/5/2037SIFToptimizationSWIRVISmulti-wavelengthhyperspectral |
| spellingShingle | Simon J. Buckley Aleksandra A. Sima Optimizing SIFT for Matching of Short Wave Infrared and Visible Wavelength Images Remote Sensing SIFT optimization SWIR VIS multi-wavelength hyperspectral |
| title | Optimizing SIFT for Matching of Short Wave Infrared and Visible Wavelength Images |
| title_full | Optimizing SIFT for Matching of Short Wave Infrared and Visible Wavelength Images |
| title_fullStr | Optimizing SIFT for Matching of Short Wave Infrared and Visible Wavelength Images |
| title_full_unstemmed | Optimizing SIFT for Matching of Short Wave Infrared and Visible Wavelength Images |
| title_short | Optimizing SIFT for Matching of Short Wave Infrared and Visible Wavelength Images |
| title_sort | optimizing sift for matching of short wave infrared and visible wavelength images |
| topic | SIFT optimization SWIR VIS multi-wavelength hyperspectral |
| url | http://www.mdpi.com/2072-4292/5/5/2037 |
| work_keys_str_mv | AT simonjbuckley optimizingsiftformatchingofshortwaveinfraredandvisiblewavelengthimages AT aleksandraasima optimizingsiftformatchingofshortwaveinfraredandvisiblewavelengthimages |