Separating patterns and finding the independent components of mixed signals based on non-Gaussian distribution properties

Robert S. Rand; Hao Chen; Pramod K Varshney

doi:10.1117/12.615427

16 September 2005 Separating patterns and finding the independent components of mixed signals based on non-Gaussian distribution properties

Robert S. Rand, Hao Chen, Pramod K Varshney

Author Affiliations +

Proceedings Volume 5909, Applications of Digital Image Processing XXVIII; 59091G (2005) https://doi.org/10.1117/12.615427
Event: Optics and Photonics 2005, 2005, San Diego, California, United States

Abstract

The effect of assuming and using non-Gaussian attributes of underlying source signals for separating/encoding patterns is investigated, for application to terrain categorization (TERCAT) problems. Our analysis provides transformed data, denoted as "Independent Components," which can be used and interpreted in different ways. The basis vectors of the resulting transformed data are statistically independent and tend to align themselves with source signals. In this effort, we investigate the basic formulation designed to transform signals for subsequent processing or analysis, as well as a more sophisticated model designed specifically for unsupervised classification. Mixes of single band images are used, as well as simulated color infrared and Landsat. A number of experiments are performed. We first validate the basic formulation using a straightforward application of the method to unmix signal data in image space. We next show the advantage of using this transformed data compared to the original data for visually detecting TERCAT targets of interest. Subsequently, we test two methods of performing unsupervised classification on a scene that contain a diverse range of terrain features, showing the benefit of these methods against a control method for TERCAT applications.

Citation Download Citation

Robert S. Rand, Hao Chen, and Pramod K Varshney "Separating patterns and finding the independent components of mixed signals based on non-Gaussian distribution properties", Proc. SPIE 5909, Applications of Digital Image Processing XXVIII, 59091G (16 September 2005); https://doi.org/10.1117/12.615427

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available