ICA mixture model algorithm for unsupervised classification of remote sensing imagery

• Published in: International journal of remote sensing Vol. 28; no. 8; pp. 1711 - 1731
• Main Authors: Shah, C. A., Varshney, P. K., Arora, M. K.
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 01.04.2007; Taylor and Francis
• Subjects: Animal, plant and microbial ecology; Applied geophysics; Biological and medical sciences; Earth sciences; Earth, ocean, space; Exact sciences and technology; Fundamental and applied biological sciences. Psychology; General aspects. Techniques; Internal geophysics; Teledetection and vegetation maps; algorithms; models; components; Maximization; density; Extraction process; Estimation; mixing; probability; Gaussian distribution; remote sensing; vegetation; Information extraction; classification; Hyperspectral imaging sensor; Mixed model; imagery; Plant cover; land cover; Pixel
• ISSN: 0143-1161; 1366-5901
• DOI: 10.1080/01431160500462121

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.