Discriminant collaborative neighborhood preserving embedding for hyperspectral imagery

Meng Lv; Xinbin Zhao; Liming Liu; Ling Jing

doi:10.1117/1.JRS.11.046004

13 October 2017 Discriminant collaborative neighborhood preserving embedding for hyperspectral imagery

Meng Lv, Xinbin Zhao, Liming Liu, Ling Jing

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 11, Issue 4, 046004 (October 2017). https://doi.org/10.1117/1.JRS.11.046004

Abstract

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 l2-graph is constructed based on the collaborative representation (CR). The edge weights of graph are calculated by l2-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 l2-graph. Experimental results on three real HSI datasets certify its effectiveness of dimensionality reduction by showing a better classification performance.

Citation Download Citation

Meng Lv, Xinbin Zhao, Liming Liu, and Ling Jing "Discriminant collaborative neighborhood preserving embedding for hyperspectral imagery," Journal of Applied Remote Sensing 11(4), 046004 (13 October 2017). https://doi.org/10.1117/1.JRS.11.046004

Received: 1 June 2017; Accepted: 21 September 2017; Published: 13 October 2017

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