TY - CONF T1 - Kernel fully constrained least squares abundance estimates T2 - Geoscience and Remote Sensing Symposium, 2007. IGARSS 2007. IEEE International Y1 - 2007 A1 - Broadwater, J. A1 - Chellapa, Rama A1 - Banerjee, A. A1 - Burlina, P. KW - abundance KW - algorithm;kernel KW - analysis; KW - AVIRIS KW - based KW - constrained KW - constraint;feature KW - constraint;spectral KW - estimates;linear KW - extraction;geophysical KW - feature KW - fully KW - image;hyperspectral KW - imagery;kernel KW - least KW - mixing KW - model;nonnegativity KW - processing;geophysical KW - processing;multidimensional KW - processing;spectral KW - signal KW - space;kernel KW - squares KW - techniques;image KW - unmixing;sum-to-one AB - 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. JA - Geoscience and Remote Sensing Symposium, 2007. IGARSS 2007. IEEE International M3 - 10.1109/IGARSS.2007.4423736 ER -