Improved feature selection based on a mutual information measure for hyperspectral image classification

Hyperspectral images contain a large amount of information which presents a major challenge for efficient classification. In this paper the information content of each spectral band is analyzed and an improved feature selection technique is proposed for the minimization of dependent information while maximizing the relevancy based on normalized mutual information (NMI). Experimental results are provided for comparisons among some relevant and recentmethods for hyperspectral feature selection in terms of their classification accuracy using real hyperspectral images.