Unsupervised classification of hyperspectral data: an ICA mixture model based approach

Abstract

Conventional unsupervised classification algorithms that model the data in each class with a multivariate Gaussian distribution are often inappropriate, as this assumption is frequently not satisfied by the remote sensing data. In this Letter, a new algorithm based on independent component analysis (ICA) is presented. The ICA mixture model (ICAMM) algorithm that models class distributions as non-Gaussian densities has been employed for unsupervised classification of a test image from the AVIRIS sensor. A number of feature-extraction techniques have also been examined that serve as a pre-processing step to reduce the dimensionality of the hyperspectral data. The proposed ICAMM algorithm results in significant increase in the classification accuracy over that obtained from the conventional K-means algorithm for land cover classification.

Acknowledgments

This research was supported by NASA under grant number NAG5-11227. M.K.A. is thankful to I.I.T. Rorkee, India, for granting a leave that enabled him to pursue post-doctoral research at Syracuse University.