Segmented minimum noise fraction transformation for efficient feature extraction of hyperspectral images

• A segmented minimum noise fraction (MNF) transformation is proposed for efficient feature extraction of hyperspectral images (HSIs).
• The proposed method significantly reduces the transformation time in comparison with the conventional MNF.
• The class separability of the extracted features is improved.
• The extracted features by SMNF even exhibit higher classification accuracy compared with the PCA or MNF.

In this paper, a segmented minimum noise fraction (MNF) transformation is proposed for efficient feature extraction of hyperspectral images (HSIs). The original bands can be partitioned into several highly correlated subgroups based on the correlation matrix image of the hyperspectral data. The MNF is implemented separately on each subgroup of the data, and then, the Bhattacharyya distance is used as the band separability measure for feature extraction. Consequently, the extracted features can then be significantly classified using state-of-art classifiers, i.e., k-NN or SVM. Experiments on two benchmark HSIs collected by AVIRIS and ROSIS demonstrate that the proposed method significantly reduces the transformation time in comparison with the conventional MNF. The Fisher scalars’ criterion shows that the class separability with the segmented MNF is the best, and the extracted features even exhibit higher classification accuracy compared with the PCA or MNF.