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

Authors: Hasna Nhaila; Elkebir Sarhrouni; Ahmed Hammouch

Addresses: Electrical Engineering Department, LRGE, ENSET, Mohammed V University in Rabat, 10100, Morocco ' Electrical Engineering Department, LRGE, ENSET, Mohammed V University in Rabat, 10100, Morocco ' Electrical Engineering Department, LRGE, ENSET, Mohammed V University in Rabat, 10100, Morocco

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.

DOI: 10.1504/IJSISE.2018.093824

International Journal of Signal and Imaging Systems Engineering, 2018 Vol.11 No.4, pp.193 - 205

Received: 04 Jul 2017
Accepted: 26 Mar 2018
Published online: 06 Aug 2018 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article