Feature extraction based on supervised locally linear embedding for classification of hyperspectral images

Abstract

Hyperspectral image has high spectral dimension,vast data and altitudinal interband redundancy,which brings problems to image classification.To effectively reduce dimensionality and improve classification precision,a new extraction method of nonlinear manifold learning feature based on Supervised Local Linear Embedding(SLLE) for classification of hyperspectral image was proposed in this paper.A data point's k Nearest Neighbours(NN) were found by using new distance function which was proposed according to prior class-label information.Because the intra-class distance is smaller than inter-class distance,classification is easy for SLLE algorithm.The experimental results on hyperspectral datasets and UCI data set demonstrate the effectiveness of the presented method.