太阳成8722(中国)有限公司-GREEN NO.1

标题: Piecewise Adaptive-Norm Trend Filtering Method for ICESat/GLAS Waveform Data Denoising

作者: Li, LY (Li, Lianying); Cai, MR (Cai, Mengrong); Guan, XB (Guan, Xiaobin); Chu, D (Chu, Dong)

来源出版物: IEEE ACCESS 卷: 8 页: 168965-168979 DOI: 10.1109/ACCESS.2020.3022886 出版年: 2020

摘要: The Geoscience Laser Altimeter System (GLAS) aboard Ice, Cloud and land Elevation Satellite (ICESat) was able to capture the full waveform of backscattered laser pulse. However, the accuracy of the surface information extracted from the waveform was vulnerable to background noise. In this paper, a piecewise adaptive l(q)-norm trend filtering method is proposed for the GLAS full waveform denoising on the basis of trend filtering. To minimize the loss of useful signal while removing the noise, the proposed method adaptively assigns different norms to the smooth constraints according to the local signal energy. The filtered results can then be obtained by iteratively minimizing the hybrid-norm loss function. The proposed method is tested on both the simulated waveforms and real GLAS waveform data. In the simulated experiments, the quantitative evaluation is conducted with the filtered waveforms, as well as the results after waveform decomposition. For comparison, the most commonly used waveform filtering methods, i.e. Gaussian filtering, wavelet transform, Empirical model decomposition and l(1) trend filtering, are involved in the experiments. The results show that the proposed method outperforms the mainstream methods on waveform filtering, in terms of removing noise and preserving the shape and energy amplitude of the GLAS waveforms.

入藏号: WOS:000572902200001

语言: English

文献类型: Article

作者关键词: ICESat/GLAS; full waveform filtering; adaptive norm; signal processing

地址: [Li, Lianying; Cai, Mengrong; Guan, Xiaobin; Chu, Dong] Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China.

通讯作者地址: Guan, XB (通讯作者)，Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China.

电子邮件地址: guanxb@whu.edu.cn

影响因子：3.745