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Proceedings Article

ICA mixture model for unsupervised classification of non-Gaussian classes in multi/hyperspectral imagery

[+] Author Affiliations
Chintan A. Shah, Manoj K. Arora, Pramod K. Varshney

Syracuse Univ. (USA)

Proc. SPIE 5093, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery IX, 569 (September 24, 2003); doi:10.1117/12.486382
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From Conference Volume 5093

  • Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery IX
  • Sylvia S. Shen; Paul E. Lewis
  • Orlando, FL | April 21, 2003

abstract

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.

© (2003) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
Citation

Chintan A. Shah ; Manoj K. Arora and Pramod K. Varshney
"ICA mixture model for unsupervised classification of non-Gaussian classes in multi/hyperspectral imagery", Proc. SPIE 5093, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery IX, 569 (September 24, 2003); doi:10.1117/12.486382; http://dx.doi.org/10.1117/12.486382


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