Featured Clustering and Ranking-Based Bad Cluster Removal for Hyperspectral Band Selection and Classification Using Ensemble of Binary SVM Classifiers

Abstract

The rich spectral and spatial information of hyperspectral images are well known in the literature. The higher dimensionality of HSI creates Hughes's effect and increased computational complexity. This demands reduction for HS images as a pre-processing step. The necessary reduction of bands can be achieved by a proper band selection (BS) technique. The proposed features based unsupervised BS technique follows three subsequent steps: 1) for each band image statistical features are extracted, 2) bands are clustered with a k-means approach using the extracted features, 3) each cluster is ranked using mean entropy measure, 4) bad clusters are removed, and 5) for each selected cluster, a representative band is selected. The proposed method is validated over three widely used standard datasets and six state-of-the-art approaches using an ensemble of binary SVM classifiers. The obtained results strongly suggest the clustering is essential to reduce the redundancy, and removal of cluster is informative to keep the informative bands.