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Hyperspectral image classification based on local binary patterns and PCANet
Author(s): Huizhen Yang; Feng Gao; Junyu Dong; Yang Yang
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Paper Abstract

Hyperspectral image classification has been well acknowledged as one of the challenging tasks of hyperspectral data processing. In this paper, we propose a novel hyperspectral image classification framework based on local binary pattern (LBP) features and PCANet. In the proposed method, linear prediction error (LPE) is first employed to select a subset of informative bands, and LBP is utilized to extract texture features. Then, spectral and texture features are stacked into a high dimensional vectors. Next, the extracted features of a specified position are transformed to a 2-D image. The obtained images of all pixels are fed into PCANet for classification. Experimental results on real hyperspectral dataset demonstrate the effectiveness of the proposed method.

Paper Details

Date Published: 10 April 2018
PDF: 8 pages
Proc. SPIE 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 106152X (10 April 2018); doi: 10.1117/12.2302769
Show Author Affiliations
Huizhen Yang, Ocean Univ. of China (China)
Feng Gao, Ocean Univ. of China (China)
Junyu Dong, Ocean Univ. of China (China)
Yang Yang, Ocean Univ. of China (China)

Published in SPIE Proceedings Vol. 10615:
Ninth International Conference on Graphic and Image Processing (ICGIP 2017)
Hui Yu; Junyu Dong, Editor(s)

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