首页>科学研究>论文专著

刘玉等:An Information Entropy-Based Sensitivity Analysis of Radar Sensing of Rough Surface

作者:来源:发布时间:2018-05-08
 An Information Entropy-Based Sensitivity Analysis of Radar Sensing of Rough Surface
作者:Liu, Y (Liu, Yu)[ 1 ] ; Chen, KS (Chen, Kun-Shan)[ 1 ]
REMOTE SENSING
卷: 10  期: 2
文献号: 286
DOI: 10.3390/rs10020286
出版年: FEB 2018
文献类型:Article
摘要
We apply Shannon entropy, an information content measure, in sensitivity analysis (SA), stemming from the fact that the essence of SA is to preserve the maximum information content of the parameters of interest that are inverted from the radar response. Then, the sensitivity to the observation configuration and surface parameters is subsequently analyzed. Attempts are also made to explore advantages, by maximizing the information content, of dual-polarization and multi-angle in improving the parameter retrieval from radar sensing of rough surface. Simulation results show that the entropy is a good indicator of the sensitivity of the radar response to the surface parameter, as it contains information on not only the probability distribution of the scattering coefficient but also on its deviation. By information entropy, richer details, to large extent, on the scattering behavior are offered through quantitatively predicting the scattering signal saturation, evaluating the effect of using multi-polarization and multi-angle observation configuration, and identifying non-significant variables. It is found that Shannon entropy, compared to Renyi entropy, appears to better represent the sensitivity with respect to monotonic variation and narrower parameter ranges. The proposed entropy-based SA method helps to deepen our understanding of the microwave scattering behavior in response to surface parameters.
通讯作者地址: Chen, KS (通讯作者)
Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China.
地址:
[ 1 ] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China
附件下载