Evaluating the effect of band subset selection in SLIC superpixel segmentation

Abstract

The Simple Linear Iterative Clustering (SLIC) algorithm is widely used for superpixel segmentation in hyperspectral image processing. In this paper, we study the effect of band-subset selection as a dimensionality reduction pre-processing step for SLIC superpixel segmentation. Column subset selection based band subset selection methods are studied. The quality of the resulting SLIC superpixel segmentation by the homogeneity of the resulting superpixels. A superpixel is considered homogeneous if the matrix resulting from unfolding the spectral signatures in the superpixel is a nearly rank one. The homogeneity ratio (number of homogeneous superpixels over total number of superpixels in the image) is used as a performance metric to compare different SLIC segmentation results. Experiments using the HYDICE Urban hyperspectral image are presented. Results show a slight increase in the homogeneity ratio for small numbers of bands (3-6) over SLIC using all bands.