Hyperspectral image classification method based on neighborhood spectra and probability cooperative representation

QI Yongfeng; MA Zhongyu

doi:10.7510/jgjs.issn.1001-3806.2019.04.003

LASER TECHNOLOGY > 2019 > 43(4): 448-452. > DOI: 10.7510/jgjs.issn.1001-3806.2019.04.003

QI Yongfeng, MA Zhongyu. Hyperspectral image classification method based on neighborhood spectra and probability cooperative representation[J]. LASER TECHNOLOGY, 2019, 43(4): 448-452. DOI: 10.7510/jgjs.issn.1001-3806.2019.04.003

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Hyperspectral image classification method based on neighborhood spectra and probability cooperative representation

College of Computer Science and Engineering, Northwest Normal University, Lanzhou 730070, China

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Received Date: September 10, 2018
Revised Date: November 11, 2018
Published Date: July 24, 2019

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Abstract

Abstract

In order to improve classification accuracy of hyperspectral remote sensing images, a classification method based on spatial information and spectral information was proposed by combining pixel neighborhood spectrum with probability co-representation method. Firstly, the neighborhood spectrum of pixels was generated by interpolation method. Then, the probability cooperative representation method was used to classify the samples to be tested. By using the proposed method, classification experiments were carried out on AVIRIS Indian Pines and Salinas scene hyperspectral remote sensing databases, compared with principal component analysis, support vector machine, sparse representation classifier and cooperative representation classifier. The results show that, the recognition accuracy of the proposed method on AVIRIS Indian Pines database is about 17% higher than that of the principal component analysis method. Its recognition accuracy and kappa coefficient are better than those of the other four methods. This method is a good classification method for hyperspectral remote sensing images.
- remote sensing,
- neighborhood spectrum,
- probability cooperative representation,
- classification

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References (24)

References

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