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Proceedings Paper

Supervised nonparametric sparse discriminant analysis for hyperspectral imagery classification
Author(s): Longfei Wu; Hao Sun; Kefeng Ji
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Paper Abstract

Owing to the high spectral sampling, the spectral information in hyperspectral imagery (HSI) is often highly correlated and contains redundancy. Motivated by the recent success of sparsity preserving based dimensionality reduction (DR) techniques in both computer vision and remote sensing image analysis community, a novel supervised nonparametric sparse discriminant analysis (NSDA) algorithm is presented for HSI classification. The objective function of NSDA aims at preserving the within-class sparse reconstructive relationship for within-class compactness characterization and maximizing the nonparametric between-class scatter simultaneously to enhance discriminative ability of the features in the projected space. Essentially, it seeks for the optimal projection matrix to identify the underlying discriminative manifold structure of a multiclass dataset. Experimental results on one visualization dataset and three recorded HSI dataset demonstrate NSDA outperforms several state-of-the-art feature extraction methods for HSI classification.

Paper Details

Date Published: 2 March 2016
PDF: 8 pages
Proc. SPIE 9901, 2nd ISPRS International Conference on Computer Vision in Remote Sensing (CVRS 2015), 990119 (2 March 2016); doi: 10.1117/12.2234944
Show Author Affiliations
Longfei Wu, National Univ. of Defense Technology (China)
Hao Sun, National Univ. of Defense Technology (China)
Kefeng Ji, National Univ. of Defense Technology (China)


Published in SPIE Proceedings Vol. 9901:
2nd ISPRS International Conference on Computer Vision in Remote Sensing (CVRS 2015)
Cheng Wang; Rongrong Ji; Chenglu Wen, Editor(s)

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