Unsupervised hyperspectral image analysis using independent component analysis (ICA)

Conference · Fri Jun 30 04:00:00 EDT 2000

OSTI ID:758100

Chiang, S S ^[1]; Ginsberg, I W

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1. RSL Nellis Bechtel Nevada

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.

• Research Organization: Bechtel Nevada Corp. (US)
• Sponsoring Organization: US Department of Energy (US)
• DOE Contract Number: AC08-96NV11718
• OSTI ID: 758100
• Report Number(s): DOE/NV/11718--433
• Country of Publication: United States
• Language: English

Related Subjects

99 GENERAL AND MISCELLANEOUS
ALGORITHMS
CLASSIFICATION
DATA ANALYSIS
IMAGE PROCESSING
LEARNING
MATRICES