Title: Semi-supervised feature selection with sparse representation for hyperspectral image classification

Authors: Yanyan Zhang; Shiguo Chen; Cailing Wang; Zhisong Pan; Daoqiang Zhang

Addresses: College of Command Information System, PLA University of Science and Technology, Nanjing, China ' Department of Computer Science and Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China ' College of Command Information System, PLA University of Science and Technology, Nanjing, China ' College of Command Information System, PLA University of Science and Technology, Nanjing, China ' Department of Computer Science and Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China

Abstract: Dimensionality reduction is one of the most important steps for remotely sensed hyperspectral image classification. Feature selection as a kind of dimensionality reduction has attracted great attentions in the recent decades. In this paper, we proposed a novel feature selection method for hyperspectral image classification based on semi-supervised learning and sparsity score (or briefly called semi-supervised sparsity score (semi-SS)). In semi-SS, the pairwise constraints instead of class labels are used as the supervision information. Furthermore, the features chosen by Semi-SS have the ability to reconstruct the original data and sparsity preserving. Experiments conducted on two famous hyperspectral data sets illustrate that the proposed algorithm is remarkably effective in comparison to the existing feature selection methods.

Keywords: hyperspectral image classification; semi-supervised feature selection; sparse representation; pairwise constraints.

DOI: 10.1504/IJMISSP.2017.088174

International Journal of Machine Intelligence and Sensory Signal Processing, 2017 Vol.2 No.1, pp.67 - 79

Received: 31 Mar 2017
Accepted: 02 Apr 2017
Published online: 27 Nov 2017 *

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