4 October 2012 Parameters optimization for wavelet denoising based on normalized spectral angle and threshold constraint machine learning
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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.
© 2012 Society of Photo-Optical Instrumentation Engineers (SPIE)
Hao Li, Hao Li, Yong Ma, Yong Ma, Kun Liang, Kun Liang, Yong Tian, Yong Tian, Rui Wang, Rui Wang, } "Parameters optimization for wavelet denoising based on normalized spectral angle and threshold constraint machine learning," Journal of Applied Remote Sensing 6(1), 063579 (4 October 2012). https://doi.org/10.1117/1.JRS.6.063579 . Submission:
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