Hyperspectral anomaly detection algorithm based on spectral angle background purification

WANG Qianghui; HUA Wenshen; HUANG Fuyu; YAN Yang; ZHANG Yan; SUO Wenkai

doi:10.7510/jgjs.issn.1001-3806.2020.05.016

LASER TECHNOLOGY > 2020 > 44(5): 623-627. > DOI: 10.7510/jgjs.issn.1001-3806.2020.05.016

WANG Qianghui, HUA Wenshen, HUANG Fuyu, YAN Yang, ZHANG Yan, SUO Wenkai. Hyperspectral anomaly detection algorithm based on spectral angle background purification[J]. LASER TECHNOLOGY, 2020, 44(5): 623-627. DOI: 10.7510/jgjs.issn.1001-3806.2020.05.016

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Hyperspectral anomaly detection algorithm based on spectral angle background purification

1.
Department of Electronic and Optical Engineering, Shijiazhuang Campus, Army Engineering University of PLA, Shijiazhuang 050003, China
2.
2 31681 Unit, People's Liberation Army of China, Tianshui 741000, China
3.
3 68129 Unit, People's Liberation Army of China, Lanzhou 730000, China

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Received Date: September 10, 2019
Revised Date: November 19, 2019
Published Date: September 24, 2020

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Abstract

Abstract

In order to solve the problem of inaccurate results and high false alarm rate when using hyperspectral image for anomaly detection, an anomaly detection algorithm based on spectral angle background purification was proposed. With this algorithm, which is based on the local RX algorithm, the the anomalous components in the background pixels between the inner and outer windows could be separated according to the spectral angular distance. The purified background pixels were then obtained, following which the anomaly detection could be performed. In order to verify the effectiveness of the algorithm, two sets of airborne visible infrared imaging spectrometer real hyperspectral data were selected for simulation experiments. The corresponding data was then compared with that of the classical global RX and local RX algorithms. The results show that, the area under the curve of the two sets of data is respectively increased by 0.0317 and 0.0053 compared with that of the local RX algorithm. These results provide a reference for the next research direction.
- spectroscopy,
- hyperspectral image,
- anomaly detection,
- spectral angle,
- background purification,
- local RX algorithm

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