Two-stage band selection algorithm for hyperspectral imagery

This paper presents a two-stage band optimal band selection algorithm for hyperspectral imagery. The algorithm tries to compute the closest subset of bands to the principal components in the sense of having the smallest canonical correlation. The first stage of the algorithm computes and initial guess for the closest bands using matrix-factorization-based band subset selection. The second stage refines the subset of bands using a steepest ascent algorithm. Experimental results using AVIRIS imagery from the Cuprite Mining District are presented.