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Journal of Applied Remote Sensing

Dimensionality reduction method based on a tensor model
Author(s): Ronghua Yan; Jinye Peng; Dongmei Ma; Desheng Wen
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Paper Abstract

Dimensionality reduction is a preprocessing step for hyperspectral image (HSI) classification. Principal component analysis reduces the spectral dimension and does not utilize the spatial information of an HSI. Both spatial and spectral information are used when an HSI is modeled as a tensor, that is, the noise in the spatial dimension is decreased and the dimension in a spectral dimension is reduced simultaneously. However, this model does not consider factors affecting the spectral signatures of ground objects. This means that further improving classification is very difficult. The authors propose that the spectral signatures of ground objects are the composite result of multiple factors, such as illumination, mixture, atmospheric scattering and radiation, and so on. In addition, these factors are very difficult to distinguish. Therefore, these factors are synthesized as within-class factors. Within-class factors, class factors, and pixels are selected to model a third-order tensor. Experimental results indicate that the classification accuracy of the new method is higher than that of the previous methods.

Paper Details

Date Published: 31 May 2017
PDF: 13 pages
J. Appl. Rem. Sens. 11(2) 025011 doi: 10.1117/1.JRS.11.025011
Published in: Journal of Applied Remote Sensing Volume 11, Issue 2
Show Author Affiliations
Ronghua Yan, Northwestern Polytechnical Univ. (China)
Xi’an Institute of Optics and Precision Mechanics (China)
Jinye Peng, Northwestern Polytechnical Univ. (China)
Northwest Univ. (China)
Dongmei Ma, Xian Janssen Pharmaceutical Ltd. (China)
Desheng Wen, Xi'an Institute of Optics and Precision Mechanics, CAS (China)

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