ICA mixture model algorithm for unsupervised classification of remote sensing imagery

C. A. Shah, P. K. Varshney, M. K. Arora

Research output: Contribution to journalArticle

20 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)1711-1731
Number of pages21
JournalInternational Journal of Remote Sensing
Volume28
Issue number8
DOIs
StatePublished - Jan 2007

ASJC Scopus subject areas

  • Earth and Planetary Sciences(all)

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