2 March 2016 Supervised nonparametric sparse discriminant analysis for hyperspectral imagery classification
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Proceedings Volume 9901, 2nd ISPRS International Conference on Computer Vision in Remote Sensing (CVRS 2015); 990119 (2016) https://doi.org/10.1117/12.2234944
Event: 2015 ISPRS International Conference on Computer Vision in Remote Sensing, 2015, Xiamen, China
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.
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Longfei Wu, Longfei Wu, Hao Sun, Hao Sun, Kefeng Ji, Kefeng Ji, "Supervised nonparametric sparse discriminant analysis for hyperspectral imagery classification", Proc. SPIE 9901, 2nd ISPRS International Conference on Computer Vision in Remote Sensing (CVRS 2015), 990119 (2 March 2016); doi: 10.1117/12.2234944; https://doi.org/10.1117/12.2234944
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