Intrinsic Dimensionality Estimation in Hyperspectral Imagery Using Residual and Change-Point Analyses

Abstract

The accurate estimation of the number of endmembers (NOE) in a given hyperspectral imagery plays a fundamental role in the effective classification, clustering, unmixing, and identification of the materials presenting in any remote scene. The optimal estimation of the NOE, however, is a quite challenging task, due to the inevitable combined presence of noise and outliers. In the last decade, several algorithms have been proposed to estimate the exact NOE. Nonetheless, these methods usually lead to different values for intrinsic dimensionality. These uncertainties make the user unable to determine the right intrinsic dimension. This letter proposes a statistical based method for finding the NOE in hyperspectral imagery. In the first step of this method, a number of candidates are selected using the residual analysis and change-point analysis. Then, according to application, one of these candidates can be selected. For this selection, here, an intrinsic dimensionality estimator, based on the singular value decomposition (SVD), is used to make this selection. Based on a comparison with second moment linear and outlier—geometry based estimation of NOE—affine hull (O-GENE-AH), the proposed method yields better results.