Spectral–Spatial Classification of Hyperspectral Imagery Based on Partitional Clustering Techniques

TLDR: A new spectral-spatial classification scheme for hyperspectral images is proposed that improves the classification accuracies and provides classification maps with more homogeneous regions, when compared to pixel wise classification.

Abstract: A new spectral-spatial classification scheme for hyperspectral images is proposed. The method combines the results of a pixel wise support vector machine classification and the segmentation map obtained by partitional clustering using majority voting. The ISODATA algorithm and Gaussian mixture resolving techniques are used for image clustering. Experimental results are presented for two hyperspectral airborne images. The developed classification scheme improves the classification accuracies and provides classification maps with more homogeneous regions, when compared to pixel wise classification. The proposed method performs particularly well for classification of images with large spatial structures and when different classes have dissimilar spectral responses and a comparable number of pixels.