Unsupervised feature extraction based on a mutual information measure for hyperspectral image classification

Finding the most informative features from high dimensional space for reliable class data modeling is one of the most challenging problems in hyperspectral image classification. The problem can be address using two basic techniques: feature selection and feature extraction. One of the most popular feature extraction methods is Principal Component Analysis (PCA), however its components are not always suitable for classification. In this paper, we present a feature reduction method (MI-PCA) which uses a nonparametric mutual information (MI) measure on the components obtained via PCA. Supervised classification results using a hyperspectral data set confirm that the new MI-PCA technique provides better classification accuracy by selecting more relevant features than when using either PCA or MI on the original data.