USING BAND SUBSET SELECTION FOR DIMENSIONALITY REDUCTION IN SUPERPIXEL SEGMENTATION OF HYPERSPECTRAL IMAGERY

This paper explores the use of unsupervised band subset selection (BSS) methods as a dimensionality reduction pre-processing stage in SLIC superpixel segmentation (BSS-SLIC). Several methods for column subset selection (CSS) are used for unsupervised band subset selection and the performance of the corresponding BSS-SLIC combination is studied. CSS is the problem of selecting the most independent columns of a matrix. BSS-SLIC superpixel segmentation results are evaluated in terms of the homogeneity of the resulting superpixels. Numerical experiments with HYDICE Urban and ROSIS Pavia data sets are used to study the performance of different BSS-SLIC algorithms. The quality of the resulting segmentation is evaluated by looking at the fraction of the total number of superpixels that are homogeneous. BSS-SLIC results in the higher percentage of homogeneous superpixels when compared with SLIC using all bands.