Potential of hybridization methods to reducing the dimensionality for multispectral biological images

We address the problem of unsupervised band reduction in multispectral imagery. We propose to use a new hybridization of dimensionality reduction method by combining two categories of bands selection method with projection method and apply it to multispectral data. The algorithm employs the concepts of fuzziness and belongingness (Fuzzy K-means) to provide a better and more adaptive clustering process. However, the Fuzzy hybridized algorithm is applicable to medical imagery. A cluster validity function associated with Bezdek's partition coefficient is employed for evaluation of the dimension reduction's performance for this multispectral data. Experiments conducted in this paper confirm the feasibility of the new hybridization for multispectral dimensionality reduction and shows the potential of the proposed approach.