Distributed source separation algorithms for hyperspectral image processing

Stefan A. Robila

doi:10.1117/12.541892

12 August 2004 Distributed source separation algorithms for hyperspectral image processing

Stefan A. Robila

Proceedings Volume 5425, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery X; (2004) https://doi.org/10.1117/12.541892
Event: Defense and Security, 2004, Orlando, Florida, United States

Abstract

This paper describes a new algorithm for feature extraction on hyperspectral images based on blind source separation (BSS) and distributed processing. I use Independent Component Analysis (ICA), a particular case of BSS, where, given a linear mixture of statistical independent sources, the goal is to recover these components by producing the unmixing matrix. In the multispectral/hyperspectral imagery, the separated components can be associated with features present in the image, the source separation algorithm projecting them in different image bands. ICA based methods have been employed for target detection and classification of hyperspectral images. However, these methods involve an iterative optimization process. When applied to hyperspectral data, this iteration results in significant execution times. The time efficiency of the method is improved by running it on a distributed environment while preserving the accuracy of the results. The design of the distributed algorithm as well as issues related to the distributed modeling of the hyperspectral data were taken in consideration and presented. The effectiveness of the proposed algorithm has been tested by comparison to the sequential source separation algorithm using data from AVIRIS and HYDICE. Preliminary results indicate that, while the accuracy of the results is preserved, the new algorithm provides a considerable speed-up in processing.

Citation Download Citation

Stefan A. Robila "Distributed source separation algorithms for hyperspectral image processing", Proc. SPIE 5425, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery X, (12 August 2004); https://doi.org/10.1117/12.541892

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