Issue |
MATEC Web Conf.
Volume 232, 2018
2018 2nd International Conference on Electronic Information Technology and Computer Engineering (EITCE 2018)
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Article Number | 02054 | |
Number of page(s) | 4 | |
Section | 3D Images Reconstruction and Virtual System | |
DOI | https://doi.org/10.1051/matecconf/201823202054 | |
Published online | 19 November 2018 |
Anomaly targets detection of hyperspectral imagery based on wavelet transform and sparse representation
College of Mechanical and Electrical Engineering, Daqing Normal University, Daqing 163712, China
a Corresponding author: chengbaozhigy@163.com
The research of anomaly target detection algorithm in hyperspectral imagery is a hot issue, which has important research value. In order to overcome low efficiency of current anomaly target detection in hyperspectral image, an anomaly detection algorithm for hyperspectral images based on wavelet transform and sparse representation was proposed. Firstly, two-dimensional discrete wavelet transform is used to denoise the hyperspectral image, and the new hyperspectral image data are obtained. Then, the results of anomaly target detection are obtained by using sparse representation theory. The real AVIRIS hyperspectral imagery data sets are used in the experiments. The results show that the detection accuracy and false alarm rate of the propoesd algorithm are better than RX and KRX algorithm.
© The Authors, published by EDP Sciences, 2018
This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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