Phase correlation based redundancy removal in feature weighting band selection for hyperspectral images

Abstract

Feature weighting based band selection provides a computationally undemanding approach to reduce the number of hyperspectral bands in order to decrease the computational requirements for processing large hyperspectral data sets. In a recent feature weighting based band selection method, a pair-wise separability criterion and matrix coefficients analysis are used to assign weights to original bands, after which bands identified to be redundant using cross correlation are removed, as it is noted that feature weighting itself does not consider spectral correlation. In the present work, it is proposed to use phase correlation instead of conventional cross correlation to remove redundant bands in the last step of feature weighting based hyperspectral band selection. Support Vector Machine (SVM) based classification of hyperspectral data with a reduced number of bands is used to evaluate the classification accuracy obtained with the proposed approach, and it is shown that feature weighting band selection with the proposed phase correlation based redundant band removal method provides increased classification accuracy compared to feature weighting band selection with conventional cross correlation based redundant band removal.