Research Papers

Parameters optimization for wavelet denoising based on normalized spectral angle and threshold constraint machine learning

[+] Author Affiliations
Hao Li

Huazhong University of Science and Technology, Department of Electronics and Information Engineering, 1037 Luoyu Road, Wuhan 430074, China

Yong Ma

Huazhong University of Science and Technology, Department of Electronics and Information Engineering, 1037 Luoyu Road, Wuhan 430074, China

Kun Liang

Huazhong University of Science and Technology, Department of Electronics and Information Engineering, 1037 Luoyu Road, Wuhan 430074, China

Yong Tian

Huazhong University of Science and Technology, Department of Electronics and Information Engineering, 1037 Luoyu Road, Wuhan 430074, China

Rui Wang

Huazhong University of Science and Technology, Department of Electronics and Information Engineering, 1037 Luoyu Road, Wuhan 430074, China

J. Appl. Remote Sens. 6(1), 063579 (Oct 04, 2012). doi:10.1117/1.JRS.6.063579
History: Received March 26, 2012; Revised July 14, 2012; Accepted August 20, 2012
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Abstract.  Wavelet parameters (e.g., wavelet type, level of decomposition) affect the performance of the wavelet denoising algorithm in hyperspectral applications. Current studies select the best wavelet parameters for a single spectral curve by comparing similarity criteria such as spectral angle (SA). However, the method to find the best parameters for a spectral library that contains multiple spectra has not been studied. In this paper, a criterion named normalized spectral angle (NSA) is proposed. By comparing NSA, the best combination of parameters for a spectral library can be selected. Moreover, a fast algorithm based on threshold constraint and machine learning is developed to reduce the time of a full search. After several iterations of learning, the combination of parameters that constantly surpasses a threshold is selected. The experiments proved that by using the NSA criterion, the SA values decreased significantly, and the fast algorithm could save 80% time consumption, while the denoising performance was not obviously impaired.

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© 2012 Society of Photo-Optical Instrumentation Engineers

Citation

Hao Li ; Yong Ma ; Kun Liang ; Yong Tian and Rui Wang
"Parameters optimization for wavelet denoising based on normalized spectral angle and threshold constraint machine learning", J. Appl. Remote Sens. 6(1), 063579 (Oct 04, 2012). ; http://dx.doi.org/10.1117/1.JRS.6.063579


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