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Proceedings Paper

Multispectral MWIR image classification using filters derived from independent component analysis
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Paper Abstract

It is known that spectral-spatial ICA basis functions of visible color images are similar to some processing elements in the human visual systems in that they resemble Gabor filters and show color opponencies. In this research we study combined spectral-spatial ICA basis functions of multispectral MWIR images. These ICA spectral-spatial basis functions are then used as filters to extract features from multispectral MWIR images. It is hypothesized that learning the added dimension of spectral information along with spatial characteristics of basis functions using ICA improves classification performance for multispectral MWIR images. The images are captured in the 3.0 - 5.0um, 3.7 - 4.2um and 4.0 - 4.5um bands using a multispectral MWIR camera. The phase relationship between the basis functions indicate how the extracted features from the different spectral band images can be combined. We use classification performance to compare features obtained by filtering using multispectral ICA basis functions, multispectral PCA basis functions and opponent Gabor filters.

Paper Details

Date Published: 9 April 2007
PDF: 14 pages
Proc. SPIE 6576, Independent Component Analyses, Wavelets, Unsupervised Nano-Biomimetic Sensors, and Neural Networks V, 65760B (9 April 2007); doi: 10.1117/12.719780
Show Author Affiliations
Srikant Chari, Univ. of Memphis (United States)
Carl Halford, Univ. of Memphis (United States)
Eddie Jacobs, Univ. of Memphis (United States)
Aaron Robinson, Univ. of Memphis (United States)


Published in SPIE Proceedings Vol. 6576:
Independent Component Analyses, Wavelets, Unsupervised Nano-Biomimetic Sensors, and Neural Networks V
Harold H. Szu; Jack Agee, Editor(s)

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