A comprehensive evaluation of feature selection algorithms in hyperspectral image classification

Nowadays, hyperspectral images have been an attractive subject for many researches in remote sensing area since they provide abundant information due to their wide range of spectral bands. On the one hand, classification plays a significant role in extraction of information for different applications. On the other hand, providing a huge amount of data by hyperspectral images may lead to complexity and bring some redundancy due to high correlation among the hyperspectral bands. In order to reduce the redundancy, feature selection algorithms have been carried out to remove irrelevant features to efficiently use the classifier and to achieve a significant accuracy with minimum costs. In this work, a comprehensive analysis of well known feature selection algorithms will be conducted with different classifiers on some commonly used hyperspectral datasets. The contribution of this paper is to present an extensive benchmark study on using feature selection algorithms with hyperspectral dataset. The analysis of feature selection algorithms will be carried out by considering number of training samples, classification accuracy and computational time.