Feature extraction using median–mean and feature line embedding

In this article, we propose a feature extraction method based on median–mean and feature line embedding (MMFLE) for the classification of hyperspectral images. In MMFLE, we maximize the class separability using discriminant analysis. Moreover, we remove the negative effect of outliers on the class mean using the median–mean line (MML) measurement and virtually enlarge the training set using the feature line (FL) distance metric. The experimental results on Indian Pines and University of Pavia data sets show the better performance of MMFLE compared to nearest feature line embedding (NFLE), median–mean line discriminant analysis (MMLDA), and some other feature extraction approaches in terms of classification accuracy using a small training set.

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