Strategies for dimensionality reduction in hyperspectral remote sensing: A comprehensive overview

Abstract

The technological advancements in spectroscopy give rise to acquiring data about different materials on earth's surface which can be utilized in a variety of potential applications. But, the hundreds of spectral bands are generally equipped with highly correlated information with limited training samples. This will degrade the Hyperspectral Image (HSI) classification accuracy. So Dimensionality Reduction (DR) has become inevitable and necessary step need to incorporate before HSI classification. The main contribution of this work lies in comparative study and review on dimensionality reduction techniques for Hyperspectral remote sensing image classification. The related challenges and research directions are also discussed. This study will help the researchers in the Hyperspectral remote sensing community to choose the appropriate DR technique for classification which can be useful in various real time applications.