Improved Random Projection with <tex>$K$</tex>-Means Clustering for Hyperspectral Image Classification

Abstract

Random projection based dimensionality reduction methods are particularly attractive options for hyperspectral data analysis, due to their data independent representation, reduction in computation time and storage costs, while preserving data separability and important information at lower dimensions. In this work, we combine the benefits of dimensionality reduction using random projections with feature selection using $k$ -means clustering in low dimensions to achieve a two-fold dimensionality reduction. Supervised classification using support vector machine (SVM) was done to study the classification performance. It is experimentally demonstrated that our proposed random projection based $k$ -means feature selection methods offers superior classification performance at far fewer dimensions than original data without dimensionality reduction.