A new filter for dimensionality reduction and classification of hyperspectral images using GLCM features and mutual information

TL;DR

This paper introduces a novel hyperspectral image classification method that combines spectral and spatial features using GLCM texture measures and mutual information, improving accuracy over existing methods.

Contribution

The paper presents a new dimensionality reduction and classification approach leveraging GLCM texture features and mutual information for hyperspectral images.

Findings

Outperforms state-of-the-art methods in classification accuracy

Effective integration of spectral and spatial features

Demonstrates good timing performance

Abstract

Dimensionality reduction is an important preprocessing step of the hyperspectral images classification (HSI), it is inevitable task. Some methods use feature selection or extraction algorithms based on spectral and spatial information. In this paper, we introduce a new methodology for dimensionality reduction and classification of HSI taking into account both spectral and spatial information based on mutual information. We characterise the spatial information by the texture features extracted from the grey level cooccurrence matrix (GLCM); we use Homogeneity, Contrast, Correlation and Energy. For classification, we use support vector machine (SVM). The experiments are performed on three well-known hyperspectral benchmark datasets. The proposed algorithm is compared with the state of the art methods. The obtained results of this fusion show that our method outperforms the other approaches by increasing the classification accuracy in a good timing. This method may be improved for more performance Keywords: hyperspectral images; classification; spectral and spatial features; grey level cooccurrence matrix; GLCM; mutual information; support vector machine; SVM.