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Proceedings Paper

Fast multi-spectral image registration based on a statistical learning technique
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Paper Abstract

Statistical learning techniques have been used to dramatically speed-up keypoint matching and image registration. However, they are rarely applied to multi-spectral images. Statistical learning techniques regard various intensities as distinctive patterns. Thus, corresponding features extracted from multi-spectral images are recognized as different patterns, because the features have different intensity characteristics. In order to overcome this problem, we propose a novel statistical learning method that can be extended to multi-spectral images. The proposed approach obtains responses from multiple classifiers that are trained with well-registered multi-spectral images, in contrast to earlier approaches using one classifier. The responses of corresponding features can be similarly characterized as being of the same class even though the intensities of the corresponding features are quite different. The experimental results show that our method provides good performance on multi-spectral image registration compared to current methods.

Paper Details

Date Published: 24 August 2010
PDF: 7 pages
Proc. SPIE 7810, Satellite Data Compression, Communications, and Processing VI, 78100C (24 August 2010); doi: 10.1117/12.860437
Show Author Affiliations
Taeyoung Kim, Univ. of Science and Technology (Korea, Republic of)
Myungjin Choi, Korea Aerospace Research Institute (Korea, Republic of)

Published in SPIE Proceedings Vol. 7810:
Satellite Data Compression, Communications, and Processing VI
Bormin Huang; Antonio J. Plaza; Joan Serra-Sagristà; Chulhee Lee; Yunsong Li; Shen-En Qian, Editor(s)

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