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Application and Evaluation of Wavelet-Based Denoising Method in Hyperspectral Imagery Data

文献类型： 外文期刊

作者： Yang, Hao 1 ; Zhang, Dongyan 1 ; Huang, Wenjiang 1 ; Gao, Zhongling 1 ; Yang, Xiaodong 1 ; Li, Cunjun 1 ; Wang, Jihua 1 ;

作者机构： 1.Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

关键词： imaging hyper-spectrometer;noise;filtering;wavelet analysis;quantitative evaluation

期刊名称：COMPUTER AND COMPUTING TECHNOLOGIES IN AGRICULTURE V, PT II

ISSN： 1868-4238

年卷期： 2012 年 369 卷

页码：

收录情况： SCI

摘要： The imaging hyper-spectrometer is highly susceptible to the presence of noise and its noise removal is regularly necessary before any derivative analysis. A wavelet-based(WT) method is developed to remove noise of hyperspectral imagery data, and commonly used denoising methods such as Savitzky-Golay method(SG), moving average method(MA), and median filter method(MF) are compared with it. Smoothing index(SI) and comprehensive evaluation indicator(eta) are designed to evaluate the performance of the four denoising methods quantitatively. The study is based on hyperspectral data of wheat leaves, collected by Pushbroom Imaging Spectrometer (PIS) and ASD Fieldspec-FR2500 (ASD) in the key growth periods. According to SI and eta, the denoising performance of the four methods shows that WT>SG=MA>MF and WT>MA>MF>SG, respectively. The comparison results reveal that WT works much better than the others with the SI value 0.28 and eta value 5.74E-05. So the wavelet-based method proposed in this paper is an optimal choice to filter the noise, in terms of balancing the contradiction between the smoothing and feature reservation ability.

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