ICA Mixture Model for Unsupervised Classification of non-Gaussian Classes in Multi/Hyperspectral Imagery

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

Research output: Contribution to journalConference Articlepeer-review

3 Scopus citations


Conventional remote sensing classification techniques model the data in each class with a multivariate Gaussian distribution. Inadequacy of such algorithms stems from Gaussian distribution assumption for the class-component densities, which is only an assumption rather than a demonstrable property of natural spectral classes. In this paper, we present an Independent Component Analysis (ICA) based approach for unsupervised classification of multi/hyperspectral imagery. ICA employed for a mixture model, estimates the data density in each class and models class distributions with non-Gaussian structure (i.e. leptokurtic or platykurtic p.d.f), formulating the ICA mixture model (ICAMM). It finds independent components and the mixing matrix for each class, using the extended information-maximization learning algorithm, and computes the class membership probabilities for each pixel. We apply the ICAMM for unsupervised classification of images from a multispectral sensor - Positive Systems Multi-Spectral Imager, and a hyperspectral sensor - Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). Four feature extraction techniques: Principal Component Analysis, Segmented Principal Component Analysis, Orthogonal Subspace Projection and Projection Pursuit have been considered as a preprocessing step to reduce dimensionality of the hyperspectral data. The results demonstrate that the ICAMM significantly outperforms the K-means algorithm for land cover classification of remotely sensed images.

Original languageEnglish (US)
Pages (from-to)569-580
Number of pages12
JournalProceedings of SPIE - The International Society for Optical Engineering
StatePublished - 2003
EventAlgorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery IX - Orlando, FL, United States
Duration: Apr 21 2003Apr 24 2003


  • Blind source separation (BSS)
  • ICA mixture model (ICAMM)
  • Independent component analysis (ICA)
  • Multispectral and hyperspectral imagery
  • Unsupervised classification
  • Unsupervised feature extraction

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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