Baozhi Cheng This email address is being protected from spambots. You need JavaScript enabled to view it.and Chunhui Zhao2

1College of mechanical and electrical engineering, Daqing Normal University, Daqing 163712, China
2College of Information and Communication, Harbin Engineering University, Harbin 150001, China


Received: April 5, 2019
Accepted: January 7, 2020
Publication Date: June 1, 2020

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In order to overcome low detection accuracy and high false alarm rate of anomaly target detection based on Kernel RX anomaly detector in hyperspectral imagery (HSI). The paper proposes a new HSI anomaly target detection algorithm based on optimal bands subspace that analyses the characteristics of HSI data. First, the wavelet transform method is used to denoise HSI, which makes that the background information of hyperspectral imagery is suppressed by using the advantages of wavelet transform. Then, HSI is divided for band subsets by the classical adaptive subspace method. The optimal bands are selected by using the minimum variance index in each band subsets. Finally, the Kernel RX algorithm is used to detect anomaly target from optimal band subspaces. The data sets of real AVIRIS and synthetic HSI are used in the experiments, and the results show that the proposed algorithm has strong robustness and lower false alarm probability.

Keywords: hyperspectral remote sensing imagery, anomaly target detection, wavelet transform, band subspaces



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