Information flow and controlling in regularization inversion of quantitative remote sensing
In order to minimize uncertainty of the inversed parameters to the largest extent by making full use of the limited information in remote sensing data, it is necessary to understand what the information flow in quantitative remote sensing model inversion is, thus control the information flow. Aiming at this, the paper takes the linear kernel-driven model inversion as an example. At first, the information flow in different inversion methods is calculated and analyzed, then the effect of information flow controlled by multi-stage inversion strategy is studied, finally, an information matrix based on USM is defined to control information flow in inversion. It shows that using Shannon entropy decrease of the inversed parameters can express information flow more properly. Changing the weight of a priori knowledge in inversion or fixing parameters and partitioning datasets in multi-stage inversion strategy can control information flow. In regularization inversion of remote sensing, information matrix based on USM may be a better tool for quantitatively controlling information flow.
作 者： YANG Hua XU Wangli ZHAO Hongrui CHEN Xue Wang Jindi 作者单位： YANG Hua,ZHAO Hongrui,CHEN Xue,Wang Jindi(Research Center for Remote Sensing and GIS, School of Geography, Beijing Normal University, State Key Laboratory of Remote Sensing Science, Beijing Key Laboratory for Remote Sensing of Environment and Digital Cities, Beijing 100875, China)XU Wangli(Department of Mathematics, Beijing Normal University, Beijing 100875, China)
刊 名： 中国科学D辑（英文版） SCI 英文刊名： SCIENCE IN CHINA (EARTH SCIENCES) 年，卷(期)： 2005 48(1) 分类号： P3 关键词： regularization inversion information flow Shannon entropy decrease information matrix