Discriminant collaborative neighborhood preserving embedding for hyperspectral imagery

Reducing the spectral dimension of hyperspectral data without loss of information is an important process in hyperspectral imagery. By adding the collaborative reconstructive information in linear discriminant analysis (LDA), we propose a discriminant collaborative neighborhood preserving embedding (DCNPE) for feature extraction from hyperspectral images. In the proposed DCNPE, an l 2 -graph is constructed based on the collaborative representation (CR). The edge weights of graph are calculated by l 2 -norm minimization using all samples to represent the pointed sample. The proposed DCNPE method aims to find a projection, which can preserve the CR-based reconstruction relationship of data as well as maximize the class discrimination of the data. DCNPE inherits the advantages of both LDA and CR. It not only overcomes the reduced dimension limitation of LDA but also preserves the collaborative neighborhood relations of data through l 2 -graph. Experimental results on three real HSI datasets certify its effectiveness of dimensionality reduction by showing a better classification performance.