Discriminative supervised neighborhood preserving embedding feature extraction for hyperspectral-image classification

A novel discriminative supervised neighborhood preserving embedding (DSNPE) method is proposed for feature extraction in classifying hyperspectral remote sensing imagery. DSNPE can preserve the local manifold structure and the neighborhood structure. What’s more, for each data point, DSNPE aims at pulling the neighboring points with the same class label towards it as near as possible, while simultaneously pushing the neighboring points with different labels apart from it as far as possible. Experimental results on two real hyperspectral image datasets are reported to illustrate the performance of DSNPE and to compare it with a few competing methods.