Hyperspectral image anomaly detecting based on kernel independent component analysis

Shangzhen Song; Huixin Zhou; Hanlin Qin; Kun Qian; Kuanhong Cheng; Jin Qian

doi:10.1117/12.2309936

20 February 2018 Hyperspectral image anomaly detecting based on kernel independent component analysis

Shangzhen Song, Huixin Zhou, Hanlin Qin, Kun Qian, Kuanhong Cheng, Jin Qian

Proceedings Volume 10697, Fourth Seminar on Novel Optoelectronic Detection Technology and Application; 1069710 (2018) https://doi.org/10.1117/12.2309936
Event: Fourth Seminar on Novel Optoelectronic Detection Technology and Application, 2017, Nanjing, China

Abstract

Hyperspectral image is a three-dimensional data cube which describes spatial information and spectral information of the scene. The anomaly detection technique can detect the targets which have difference between the image and the background without priori information. Kernel independent component analysis(KICA) is a method of mapping hyperspectral data into the kernel space for feature extraction. In this paper, the hyperspectral image is subjected to abnormal information detection based on KICA. First, we calculate the kernel matrix K in order to map the data to high-dimensional space for whitening and dimension reduction processing. Then we utilize the FastICA algorithm to extract the core independent component (KIC). Finally, the extracted independent components with the most abnormal information are analyzed by RX operator, kernel RX operator and abundance quantization method. Comparing with the simulation result and the detected result by RX method, the representation shows the algorithm based on KICA has better detection performance.

Citation Download Citation

Shangzhen Song, Huixin Zhou, Hanlin Qin, Kun Qian, Kuanhong Cheng, and Jin Qian "Hyperspectral image anomaly detecting based on kernel independent component analysis", Proc. SPIE 10697, Fourth Seminar on Novel Optoelectronic Detection Technology and Application, 1069710 (20 February 2018); https://doi.org/10.1117/12.2309936

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