摘要
极化干涉SAR是一种集极化和干涉SAR优势于一体的新型遥感技术。结合两层植被随机体散射模型和极化分解技术,基于极化干涉SAR数据的概率分布统计特征,提出一种利用参数迭代求解预测模型和测量值最小似然距离的植被高度反演方法。该方法克服了传统最大似然估计方法需已知地表散射特征参数的约束,减少了计算复杂性。最后通过极化干涉SAR仿真数据实验分析,文中算法相对于三阶段反演算法提高了植被高度估计的精度,验证了算法的有效性。
Polarimetrie interferometric SAR （PolInSAR） is a new type remote sensing technique which combines the advantage of polarimetric SAR and interferometric SAR. Combining the two layer random volume over ground model with the classification of the polarimetric data and from the statistical distribution characteristic of the PollnSAR data, this paper proposes an inversion algorithm of vegetation heights estimation which makes use of the parameters iteration to minimize the likelihood distance between the model forecast and the sensors observations. The proposed algorithm can overcome the restriction of traditional maximum likelihood esti- mation method which requires the parameters of ground scattering to be known, and also decreases the complexity of calculation. Finally, by analysis of height inversion for the PolInSAR simulated data, the proposed algorithm has better performance than the three stage method, therefore the validity of this method is proved.
出处
《现代雷达》
CSCD
北大核心
2009年第11期60-63,共4页
Modern Radar
关键词
合成孔径雷达
极化干涉SAR
最小似然距离
植被高度反演
SAR
polarimetric interferometric SAR
minimum likelihood distance
vegetation height inversion
