Kernel fully constrained least squares abundance estimates

TitleKernel fully constrained least squares abundance estimates
Publication TypeConference Papers
Year of Publication2007
AuthorsBroadwater J, Chellappa R, Banerjee A, Burlina P
Conference NameGeoscience and Remote Sensing Symposium, 2007. IGARSS 2007. IEEE International
Date Published2007/07//
Keywordsabundance, algorithm;kernel, analysis;, AVIRIS, based, constrained, constraint;feature, constraint;spectral, estimates;linear, extraction;geophysical, feature, fully, image;hyperspectral, imagery;kernel, least, mixing, model;nonnegativity, processing;geophysical, processing;multidimensional, processing;spectral, signal, space;kernel, squares, techniques;image, unmixing;sum-to-one

A critical step for fitting a linear mixing model to hyperspectral imagery is the estimation of the abundances. The abundances are the percentage of each end member within a given pixel; therefore, they should be non-negative and sum to one. With the advent of kernel based algorithms for hyperspectral imagery, kernel based abundance estimates have become necessary. This paper presents such an algorithm that estimates the abundances in the kernel feature space while maintaining the non-negativity and sum-to-one constraints. The usefulness of the algorithm is shown using the AVIRIS Cuprite, Nevada image.