Supervised linear manifold learning feature extraction for hyperspectral image classification

A supervised neighborhood preserving embedding (SNPE) linear manifold learning feature extraction method for hyperspectral image classification is presented in this paper. A point´s k nearest neighbors is found by using new distance which is proposed according to prior class-label information. The new distance makes intra-class more tightly and inter-class more separately. SNPE overcomes the single manifold assumption of NPE. Data sets lay on (or near) multiple manifolds can be processed. Experimental results on AVIRIS hyperspectral data set demonstrate the effectiveness of our method.