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.
ASJC Scopus subject areas
- Earth and Planetary Sciences(all)