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Proceedings Paper

Hyperspectral image classification based on local binary patterns and SVDNet
Author(s): Mengjie Sun; Feng Gao; Junyu Dong
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Paper Abstract

Hyperspectral image classification is a critical issue in hyperspectral data processing. However, the task has been acknowledged as extremely challenging due to its characteristics including high dimensionality in data, spatial variability of spectral features and scarcity of marked data. In this paper, we propose a new classification method combined with Local Binary Patterns (LBP) and Singular Value Decomposition Networks (SVDNet). Linear Prediction Error is first employed to select informative spectral bands. Then LBP is utilized to extract the texture features. After that, the extracted features of a specified field are transformed to 2-D images. Finally, SVDNet classifies the obtained images and then the classification result can be obtained. Experimental results on the real hyperspectral dataset demonstrate that the proposed method is capable to achieve higher classification accuracy or at least comparable to existing methods.

Paper Details

Date Published: 6 May 2019
PDF: 7 pages
Proc. SPIE 11069, Tenth International Conference on Graphics and Image Processing (ICGIP 2018), 110691F (6 May 2019); doi: 10.1117/12.2524372
Show Author Affiliations
Mengjie Sun, Ocean Univ. of China (China)
Feng Gao, Ocean Univ. of China (China)
Junyu Dong, Ocean Univ. of China (China)

Published in SPIE Proceedings Vol. 11069:
Tenth International Conference on Graphics and Image Processing (ICGIP 2018)
Chunming Li; Hui Yu; Zhigeng Pan; Yifei Pu, Editor(s)

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