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Title: Hyperspectral image classification based on two-stage subspace projection
Authors: Li, XY
Zhang, LF
You, JE 
Keywords: Hyperspectral image (HSI) classification
Kernel principal component analysis (KPCA)
Locality preserving projection
Discrimination information
Issue Date: 2018
Publisher: Molecular Diversity Preservation International (MDPI)
Source: Remote sensing, Oct. 2018, v. 10, no. 10, 1565, p. 1-16 How to cite?
Journal: Remote sensingonline only 
Abstract: Hyperspectral image (HSI) classification is a widely used application to provide important information of land covers. Each pixel of an HSI has hundreds of spectral bands, which are often considered as features. However, some features are highly correlated and nonlinear. To address these problems, we propose a new discrimination analysis framework for HSI classification based on the Two-stage Subspace Projection (TwoSP) in this paper. First, the proposed framework projects the original feature data into a higher-dimensional feature subspace by exploiting the kernel principal component analysis (KPCA). Then, a novel discrimination-information based locality preserving projection (DLPP) method is applied to the preceding KPCA feature data. Finally, an optimal low-dimensional feature space is constructed for the subsequent HSI classification. The main contributions of the proposed TwoSP method are twofold: (1) the discrimination information is utilized to minimize the within-class distance in a small neighborhood, and (2) the subspace found by TwoSP separates the samples more than they would be if DLPP was directly applied to the original HSI data. Experimental results on two real-world HSI datasets demonstrate the effectiveness of the proposed TwoSP method in terms of classification accuracy.
EISSN: 2072-4292
DOI: 10.3390/rs10101565
Rights: © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (
The following publication Li, X.Y., Zhang, L.F., & You, J.E. (2018). Hyperspectral image classification based on two-stage subspace projection. Remote sensing, 10 (10), 1565, p. 1-16 is available at
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