Hyperspectral image classification based on local binary patterns and PCANet

Huizhen Yang; Feng Gao; Junyu Dong; Yang Yang

doi:10.1117/12.2302769

10 April 2018 Hyperspectral image classification based on local binary patterns and PCANet

Huizhen Yang, Feng Gao, Junyu Dong, Yang Yang

Proceedings Volume 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017); 106152X (2018) https://doi.org/10.1117/12.2302769
Event: Ninth International Conference on Graphic and Image Processing, 2017, Qingdao, China

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.

Citation Download Citation

Huizhen Yang, Feng Gao, Junyu Dong, and Yang Yang "Hyperspectral image classification based on local binary patterns and PCANet", Proc. SPIE 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 106152X (10 April 2018); https://doi.org/10.1117/12.2302769

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