摘要
结合上海地区城市快鸟(QuickBird)高分辨率卫星影像,简述高分辨率影像定位中的通用传感器模型,有理多项式系数模型(RPC).通过在影像上选取均匀分布的50个地面控制点,运用正则化最小二乘迭代法计算得出RPC.然后分2种情况对求解出的PRC改正,即当同时具有原始地面控制点(GCP)和辅助GCP时,应用正则化批处理最小二乘迭代法(BILSR)来改正RPC系数,而当只有辅助GCP时,则使用增量离散卡尔曼滤波方法(IDKF)来改正RPC精度.最后利用40个检测点,设计多种方案的实验并进行分析,以此为基础对BILSR和IDKF法的可行性进行了对比分析,认为辅助控制点的选择应侧重其本身更高的精度而不是单纯从数量上考虑,此外得出城市高分辨率卫星影像几何处理的一些结论.
Based on collected QuickBird imagery in Shanghai region, the iterative least-squares solution with regularization （ILSR） are derived to determine the rational polynomial coefficients（RPCs） by using 50 fair distributed ground control points（GCPs） firstly. Two methods are then used to refine determined RPCs under different circumstance as.. （1） when both the original and the additional GCPs are available, the RPCs wll be recomputed using the batch iterative least-squares solution with regularization （BILSR） method; and （2） when only the new GCPs are available, incremental discrete Kalman filtering （IDKF） method is then described. Meanwhile, schemes are scheduled with check points to evaluate their geo- positioning accuracy. It is obtained that auxiliary points are necessary to refine the RPC because of their higher position accuracy rather than the amount used. A conclusion is then achieved as a reference when the high resolution imagery is processed in metropolitan area.
出处
《同济大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2009年第7期983-988,共6页
Journal of Tongji University:Natural Science
基金
国家自然科学基金资助项目(40501061)
国家"八六三"高技术研究发展计划资助项目(2007AA12Z178)
关键词
卫星影像
有理多项式系数
正则化批处理最小二乘迭代
增量离散卡尔曼滤波
定位精度
satellite imagery
rational polynomial coefficients
batch iterative least-squares solution with regularization
incremental discrete kalman filtering
geo- positioning accuracy
