title: Using independent component analysis for material estimation in hyperspectral images.
abstract: We develop a method for automated material estimation in hyperspectral images. The method models a hyperspectral pixel as a linear mixture of unknown materials. The method is particularly useful for applications in which material regions in a scene are smaller than one pixel. In contrast to many material estimation methods, the new method uses the statistics of large numbers of pixels rather than attempting to identify a small number of the purest pixels. The method is based on maximizing the independence of material abundances at each pixel. We show how independent component analysis algorithms can be adapted for use with this problem. We demonstrate properties of the method by application to airborne hyperspectral data.
authors.affiliation: Computer Vision Laboratory, Department of Electrical Engineering and Computer Science, University of California, Irvine, California 92697, USA.
authors.forename: Chia-Yun
authors.initials: CY
authors.lastname: Kuan
authors.forename: Glenn
authors.initials: G
authors.lastname: Healey