skip to main content

Title: Unsupervised hyperspectral image analysis using independent component analysis (ICA)

In this paper, an ICA-based approach is proposed for hyperspectral image analysis. It can be viewed as a random version of the commonly used linear spectral mixture analysis, in which the abundance fractions in a linear mixture model are considered to be unknown independent signal sources. It does not require the full rank of the separating matrix or orthogonality as most ICA methods do. More importantly, the learning algorithm is designed based on the independency of the material abundance vector rather than the independency of the separating matrix generally used to constrain the standard ICA. As a result, the designed learning algorithm is able to converge to non-orthogonal independent components. This is particularly useful in hyperspectral image analysis since many materials extracted from a hyperspectral image may have similar spectral signatures and may not be orthogonal. The AVIRIS experiments have demonstrated that the proposed ICA provides an effective unsupervised technique for hyperspectral image classification.
Authors:
;
Publication Date:
OSTI Identifier:
758100
Report Number(s):
DOE/NV/11718--433
TRN: AH200021%%321
DOE Contract Number:
AC08-96NV11718
Resource Type:
Conference
Resource Relation:
Conference: Conference title not supplied, University of Maryland, College Park, MD (US), No date supplied; Other Information: PBD: 30 Jun 2000
Research Org:
Bechtel Nevada Corp. (US)
Sponsoring Org:
US Department of Energy (US)
Country of Publication:
United States
Language:
English
Subject:
99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE; IMAGE PROCESSING; MATRICES; ALGORITHMS; LEARNING; CLASSIFICATION; DATA ANALYSIS