An Ortho-Rectification Algorithm Based on Collinearity Equation and Polynomial Error Modification for Remote Sensing Imagery

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An Ortho-Rectification Algorithm Based on Collinearity Equation and Polynomial Error Modification for Remote Sensing Imagery

更新时间：2025-07-09

- An Ortho-Rectification Algorithm Based on Collinearity Equation and Polynomial Error Modification for Remote Sensing Imagery
- Vol. 46, Issue 12, Pages: 49-54(2012)
- 作者机构：
  
  哈尔滨工业大学电子与信息工程学院,哈尔滨,150001
- 作者简介：
- 基金信息：
- DOI：
  CLC： TN391
- Online First：10 December 2012，
  
  Published：2012
- 稿件说明：
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An Ortho-Rectification Algorithm Based on Collinearity Equation and Polynomial Error Modification for Remote Sensing Imagery[J]. 2012, 46(12): 49-54.
DOI：

An Ortho-Rectification Algorithm Based on Collinearity Equation and Polynomial Error Modification for Remote Sensing Imagery[J]. 2012, 46(12): 49-54. DOI：

摘要

针对线阵推扫式遥感影像正射校正中飞行方向校正误差较大和扫描方向校正误差不均匀的问题

提出一种融合共线方程和多项式残差修正的高精度正射校正新算法(CPOR).该算法首先在传统共线方程的基础上增加一个循环迭代步骤对每扫描行的外方位元素进行精确解算

然后对像平面坐标进行初次校正估计; 之后利用控制点处的估计偏差构造多项式误差方程

对每个像坐标进行残差估计并修正

最终完成正射校正过程.实验结果表明

CPOR算法经过循环迭代求解外方位元素和多项式修正估计残差

使校正误差在行方向和列方向都呈均匀分布

并使整体校正误差小于2个像元

平均校正误差较之其他传统校正方法提高了5～6像素.

Abstract

The existing ortho-rectification algorithms for linear-pushbroom scanning imagery generally result in the high correction error in flight direction and the uneven correction error of quite big breadth image in the scanning direction. A new algorithm based on amalgamation of collinearity equation and polynomial(CPOR)is proposed for highly accurate ortho-rectification. Iterations are conducted as an additional step in the traditional collinearity equation to improve the calculating precision of orientation parameters of each scan line and to estimate image coordinates. Then a polynomial error equation is constructed using estimated errors of control points to modify the error of each coordinate. Experimental results show that the rectification errors of the CPOR algorithm in both the flight direction and the scanning direction distribute more uniformly. The overall errors are less than 2 pixels

and the average errors are improved by 5-6 pixels compared with the traditional methods.

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references

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