Authors: Ibtissam Banit'ouagua; Mounir Ait Kerroum; Ahmed Hammouch; Driss Aboutajdine
Addresses: UFR LRIT, FS, Mohamed V University, B.P.1014 Rabat, Morocco ' Ibn Tofail University, B.P.242 Kenitra, Morocco ' GTI-LGE, ENSET, Mohamed V University, B.P.6207 Rabat, Morocco ' LRIT, FS, Mohamed V University, B.P.1014 Rabat, Morocco
Abstract: Band selection is one of the most important problems in hyper-spectral image classification. Indeed, the presence of irrelevant and/or redundant bands can harm the performance of classification accuracy. This paper investigates the effectiveness of four mutual information feature selector (MIFS) algorithms to select the informative bands for hyper-spectral image classification. These algorithms are: MIFS, MIFS-U, MIFS-U2 and NMIFS. Our main motivation behind the study of this family algorithm is due to the fact that mutual information (MI) is an effective indicator to measure the overall statistical dependency between variables and it has proved its efficiency in many pattern recognition problems, especially in remote sensing. The experimental results have been made on two AVIRIS hyper-spectral datasets (Indian Pines and Salinas) and prove that MIFS algorithm and its variants give promising performances, in terms of dimensionality reduction and classification accuracy than MI-est method (Guo et al., 2006), specially for high dimensional data with many irrelevant and/or redundant bands.
Keywords: band selection; mutual information; hyperspectral images; SVM classification; support vector machines; image classification; feature selection; high dimensional data; irrelevant bands; redundant bands.
International Journal of Advanced Intelligence Paradigms, 2016 Vol.8 No.1, pp.98 - 118
Received: 11 Mar 2015
Accepted: 09 Oct 2015
Published online: 17 Feb 2016 *