A novel information gain-based approach for classification and dimensionality reduction of hyperspectral images

TL;DR

This paper introduces a new information gain-based filter method for hyperspectral image classification and dimensionality reduction, effectively selecting informative bands to improve accuracy and reduce computational costs.

Contribution

The paper presents a novel filter approach using information gain for hyperspectral band selection, enhancing classification performance and efficiency over existing methods.

Findings

Outperforms three competing methods on benchmark datasets

Reduces computational cost significantly

Improves classification accuracy

Abstract

Recently, the hyperspectral sensors have improved our ability to monitor the earth surface with high spectral resolution. However, the high dimensionality of spectral data brings challenges for the image processing. Consequently, the dimensionality reduction is a necessary step in order to reduce the computational complexity and increase the classification accuracy. In this paper, we propose a new filter approach based on information gain for dimensionality reduction and classification of hyperspectral images. A special strategy based on hyperspectral bands selection is adopted to pick the most informative bands and discard the irrelevant and noisy ones. The algorithm evaluates the relevancy of the bands based on the information gain function with the support vector machine classifier. The proposed method is compared using two benchmark hyperspectral datasets (Indiana, Pavia) with three competing methods. The comparison results showed that the information gain filter approach outperforms the other methods on the tested datasets and could significantly reduce the computation cost while improving the classification accuracy. Keywords: Hyperspectral images; dimensionality reduction; information gain; classification accuracy. Keywords: Hyperspectral images; dimensionality reduction; information gain; classification accuracy.