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Journal of Applied Remote Sensing

Parameters optimization for wavelet denoising based on normalized spectral angle and threshold constraint machine learning
Author(s): Hao Li; Yong Ma; Kun Liang; Yong Tian; Rui Wang
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Paper 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.

Paper Details

Date Published: 4 October 2012
PDF: 9 pages
J. Appl. Remote Sens. 6(1) 063579 doi: 10.1117/1.JRS.6.063579
Published in: Journal of Applied Remote Sensing Volume 6, Issue 1
Show Author Affiliations
Hao Li, Huazhong Univ. of Science and Technology (China)
Yong Ma, Huazhong Univ. of Science and Technology (China)
Kun Liang, Huazhong Univ. of Science and Technology (China)
Yong Tian, Huazhong Univ. of Science and Technology (China)
Rui Wang, Huazhong Univ. of Science and Technology (China)


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