Investigation on Constrained Matrix Factorization for Hyperspectral Image Analysis

摘要

Matrix factorization is applied to unsupervised linear unmixing for hyperspectral imagery. The algorithm, called nonnegative matrix factorization, is used. It imposes a constraint on the non-negativity of the amplitudes of the recovered endmember spectral signatures as well as their fractional abundances. This ensures the recovery of physically meaningful endmembers and their abundances. This algorithm is further modified to include the sum-to-one constraint such that the sum of the fractional abundances for each pixel is unity. Several practical implementation issues in hyperspectral image unmixng are discussed. Some preliminary results from AVIRIS experiments are presented. We also discuss the advantages and possible limitations of this method in hyperspectral image analysis.