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 -