摘要
极化干涉合成孔径雷达(PolInSAR)测量是一种集极化雷达(PolSAR)和干涉雷达(InSAR)测量技术于一体的新的对地观测技术,利用极化干涉雷达数据反演地表植被参数特别是森林的垂直结构参数是当前极化干涉研究的热点问题。经典的单基线PolInSAR反演算法是Cloude和Papathanassiou提出的三阶段反演算法,但是该算法在相干值估计、直线拟合和散射体去相干估计等方面都存在着误差,直接影响反演精度。该文提出了一种新的基于统计特征和PolInSAR相位最优化算法的反演算法,并采用PolInSAR模拟数据验证了该算法的有效性。
Polarimetric Interferometric SAR （PolInSAR） is a new advanced technique recently based on measurement techniques of polarimetric SAR and interferometric SAR, and making use of PolInSAR data for retrieving the vertical structure parameters of the vegetation layer becomes the hot research topic of the PolInSAR at present. The most useful Single-baseline PolInSAR inversion algorithm is the three-stage inversion algorithm, which was proposed by Cloude and Papathartassiou, but this algorithm has the errors on the its three aspects： coherence estimation, line fitting and volume coherence estimation, and this errors straightly effect the inversion precision. This paper proposes a new inversion algorithm based on the statistic characteristic and PolInSAR phase optimization algorithm, and takes the PolInSAR simulated data to prove the validity of this algorithm.
出处
《电子与信息学报》
EI
CSCD
北大核心
2008年第7期1744-1746,共3页
Journal of Electronics & Information Technology
