Determining the dimensionality of hyperspectral imagery for unsupervised band selection

This paper addresses the problem of estimating the dimension of a hyperspectral image. Spanning and intrinsic dimension concepts are studied as ways to determine the number of degrees of freedom needed to represent a Hyperspectral Image. Algorithms for the estimation of spanning and intrinsic dimension are reviewed and applied to hyperspectral images. Estimators are evaluated and compared using simulated and AVIRIS data. The final objective of this work is to develop an algorithm to determine the number of bands to select in a band subset selection algorithm.