A new band selection approach based on information theory and support vector machine for hyperspectral images reduction and classification

TL;DR

This paper introduces a novel band selection method for hyperspectral image classification that leverages joint mutual information to identify highly discriminative bands, improving computational efficiency and accuracy.

Contribution

The paper proposes a new filter-based band selection approach using joint mutual information, outperforming existing methods in hyperspectral image classification.

Findings

The proposed method achieves higher classification accuracy.

It reduces computational complexity by selecting fewer bands.

Outperforms existing mutual information-based filters.

Abstract

The high dimensionality of hyperspectral images consisting of several bands often imposes a big computational challenge for image processing. Therefore, spectral band selection is an essential step for removing the irrelevant, noisy and redundant bands. Consequently increasing the classification accuracy. However, identification of useful bands from hundreds or even thousands of related bands is a nontrivial task. This paper aims at identifying a small set of highly discriminative bands, for improving computational speed and prediction accuracy. Hence, we proposed a new strategy based on joint mutual information to measure the statistical dependence and correlation between the selected bands and evaluate the relative utility of each one to classification. The proposed filter approach is compared to an effective reproduced filters based on mutual information. Simulations results on the hyperpectral image HSI AVIRIS 92AV3C using the SVM classifier have shown that the effective proposed algorithm outperforms the reproduced filters strategy performance. Keywords-Hyperspectral images, Classification, band Selection, Joint Mutual Information, dimensionality reduction ,correlation, SVM.