A novel filter based on three variables mutual information for dimensionality reduction and classification of hyperspectral images

TL;DR

This paper introduces a new filter method using three variables mutual information for hyperspectral image band selection, improving classification accuracy by considering band relevance and interaction, and demonstrating superior performance over existing methods.

Contribution

A novel filter approach based on three variables mutual information for hyperspectral image band selection, accounting for band relevance and interaction, with demonstrated improved classification results.

Findings

Proposed method outperforms existing mutual information-based filters.

Effective in reducing dimensionality and improving classification accuracy.

Validated on AVIRIS 92AV3C dataset.

Abstract

The high dimensionality of hyperspectral images (HSI) that contains more than hundred bands (images) for the same region called Ground Truth Map, often imposes a heavy computational burden for image processing and complicates the learning process. In fact, the removal of irrelevant, noisy and redundant bands helps increase the classification accuracy. Band selection filter based on "Mutual Information" is a common technique for dimensionality reduction. In this paper, a categorization of dimensionality reduction methods according to the evaluation process is presented. Moreover, a new filter approach based on three variables mutual information is developed in order to measure band correlation for classification, it considers not only bands relevance but also bands interaction. The proposed approach is compared to a reproduced filter algorithm based on mutual information. Experimental results on HSI AVIRIS 92AV3C have shown that the proposed approach is very competitive, effective and outperforms the reproduced filter strategy performance. Keywords - Hyperspectral images, Classification, band Selection, Three variables Mutual Information, information gain.