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

Chintan A. Shah, Manoj K. Arora, Pramod K. Varshney

Research output: Contribution to journalArticlepeer-review

71 Scopus citations


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 preprocessing 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.

Original languageEnglish (US)
Pages (from-to)481-487
Number of pages7
JournalInternational Journal of Remote Sensing
Issue number2
StatePublished - Jan 20 2004

ASJC Scopus subject areas

  • General Earth and Planetary Sciences


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