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.
Conference Committee Involvement (2)
Satellite Data Compression, Communications, and Processing VIII
12 August 2012 | San Diego, California, United States
Satellite Data Compression, Communications, and Processing VI
3 August 2010 | San Diego, California, United States
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