A Multi-Layer Binary Pattern Based Method for Hyperspectral Imagery Classification Using Combined Spatial-Spectral Characteristics

ZHI Lu; YU Xuchu; ZOU Bin; LIU Bing

doi:10.13203/j.whugis20180004

Volume 44 Issue 11

Nov. 2019

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Geomatics and Information Science of Wuhan University > 2019 > 44(11): 1659-1666. > DOI: 10.13203/j.whugis20180004

ZHI Lu, YU Xuchu, ZOU Bin, LIU Bing. A Multi-Layer Binary Pattern Based Method for Hyperspectral Imagery Classification Using Combined Spatial-Spectral Characteristics[J]. Geomatics and Information Science of Wuhan University, 2019, 44(11): 1659-1666. DOI: 10.13203/j.whugis20180004

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A Multi-Layer Binary Pattern Based Method for Hyperspectral Imagery Classification Using Combined Spatial-Spectral Characteristics

ZHI Lu^1,,
YU Xuchu²,
ZOU Bin^3, ,,
LIU Bing²

1.
College of Surveying and Geo-informatics, North China University of Water Resources and Electric Power, Zhengzhou 450001, China
2.
Information Engineering University, Zhengzhou 450001, China
3.
Department of Geo-informatics, Central South University, Changsha 410083, China

Funds:

The National Key Research and Development Program of China 2016YFC0206205

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Author Bio:
ZHI Lu, PhD, specializes in hyperspectral image classification. E-mail: zhilu_361@163.com
Corresponding author:
ZOU Bin, PhD, professor. E-mail: 210010@csu.edu.cn
Received Date: June 03, 2018
Published Date: November 04, 2019

Graphical Abstract

Abstract

Abstract

It is essential to make full use of the spatial features for hyperspectral imagery classification. A multi-layer binary pattern based method for hyperspectral remote sensing imagery classification using combined spatial-spectral characteristics is proposed. Firstly, the hyperspectral remote sensing imagery was transformed to local binary pattern image reflecting the micro-structures of the imagery, and multi-layer feature vectors were acquired from the local binary pattern image reflecting a wider range of the imagery structure. Here, in order to verify the proposed method, the PaviaU, the Salinas and the Chikusei imageries were used and nine kinds of features such as spectral features, local binary pattern spatial features, the multi-resolution local binary pattern features, etc., were employed in the experiment for hyperspectral remote sensing imagery spatial-spectral classification.The classifier of this algorithm used the kernel extreme learning machine classifier. The overall classification accuracies separately reached 97.31%, 98.96% and 97.85% based on the multi-layer binary patterns spatial-spectral features which were superior to the traditional methods such as spectral classification and spatial-spectral classification using 3Gabor features. The results indicate that the proposed approach can provide the effectively distinguished features showing the higher classification accuracy and the smoother classification map.

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