ICA mixture model algorithm for unsupervised classification of remote sensing imagery

Authors:
C. A. Shah;P. K. Varshney;M. K. Arora
Affiliations:
Department of Electrical Engineering and Computer Science, Syracuse University, Syracuse, NY 13244, USA;Department of Electrical Engineering and Computer Science, Syracuse University, Syracuse, NY 13244, USA;Department of Civil Engineering, Indian Institute of Technology, Roorkee, Uttaranchal, 247667, India
Venue:
International Journal of Remote Sensing
Year:
2007

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Abstract

Conventional remote sensing classification algorithms assume that the data in each class can be modelled using a multivariate Gaussian distribution. As this assumption is often not valid in practice, conventional algorithms do not perform well. In this paper, we present an independent component analysis (ICA)-based approach for unsupervised classification of multi/hyperspectral imagery. ICA used for a mixture model estimates the data density in each class and models class distributions with non-Gaussian (sub-and super-Gaussian) probability density functions, resulting in the ICA mixture model (ICAMM) algorithm. Independent components and the mixing matrix for each class are found using an extended information-maximization algorithm, and the class membership probabilities for each pixel are computed. The pixel is allocated to the class having maximum class membership probability to produce a classification. We apply the ICAMM algorithm for unsupervised classification of images obtained from both multispectral and hyperspectral sensors. Four feature extraction techniques are considered as a preprocessing step to reduce the dimensionality of the hyperspectral data. The results demonstrate that the ICAMM algorithm significantly outperforms the conventional K-means algorithm for land cover classification produced from both multi-and hyperspectral remote sensing images.