Component analysis of spatial and spectral patterns in multispectral images. II. Entropy minimization.
In Part I [J. Opt. Soc. Am. A 4, 2101 (1987)] of this series, we developed a method for estimating both spatial patterns and spectral curves of components in a multispectral scene. This method does not need spatial and spectral information about the components but only multispread imagery data. The estimation is given as a feasible solution set satisfying the nonnegativity constraint for density and spectral response for all components at all pixels. In this paper, we estimate unique solutions for both the component patterns and the spectra from the feasible solution set. The solution is given by optimizing an entropy minimization criterion. This criterion enhances the spectral or spatial features of individual components. Two experimental results are shown to demonstrate the effectiveness of this method with biological and cytochemical specimens. The limitations of this method for unique pattern estimation are also discussed.
Sasaki
K
K
Department of Applied Physics, Osaka University, Japan.
Kawata
S
S
Minami
S
S
D000465:Algorithms; D003118:Color Perception / Q000502:physiology*; D006801:Humans; D008960:Models, Psychological; D013028:Space Perception / Q000502:physiology*