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线阵垂轨环扫式光学遥感卫星影像几何纠正 被引量:5

Geometric correction of optical remote sensing satellite images captured by linear array sensors circular scanning perpendicular to the orbit
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摘要 新型线阵环扫式光学遥感卫星通过垂轨环扫、沿轨拼接的成像方式实现了超大幅宽与高分辨率的兼顾,但其特殊的成像方式也导致影像的几何畸变十分严重,必须对其进行几何纠正。本文提出了一种适用于线阵环扫式光学遥感卫星的影像几何纠正方法,该方法首先在科学分析卫星成像特点的基础上构建线阵环扫影像的严格成像模型,然后利用影像轨道、姿态参数和开源DEM对影像进行粗纠正,最后利用参考正射影像匹配得到大量同名点作为控制点,通过样条函数拟合进行影像精纠正。为验证提出方法的有效性,利用模拟影像开展了试验,结果表明该方法几何精纠正的精度可以达到1像元。该方法能够有效解决线阵环扫式卫星影像几何畸变大的问题,提供高精度影像产品,具有重要的应用价值。 The new linear array circular scanning optical remote sensing satellite has achieved both super large breadth and high resolution through circular scanning perpendicular to the orbit and along the orbit splicing.However,its special imaging mode results in serious geometric distortion of the image,which re⁃quires correction.A geometric correction method for linear array circular scanning optical remote sensing satellite images is proposed.Firstly,a strict imaging model of a linear array circular scanning image is con⁃structed based on the scientific analysis of satellite imaging characteristics.Subsequently,the image is pre⁃liminarily corrected using image orbit,attitude parameters,and open-source DEM.Finally,several hom⁃onymous points are obtained using reference orthophoto image matching as the control points,and the im⁃age is accurately corrected using spline function fitting.To verify the effectiveness of the proposed meth⁃od,experiments are performed using simulated images,and the results show that the accuracy of the pro⁃posed method can reach 1 pixel.This method can effectively solve the issue of large geometric distortion of linear array circular scanning satellite images,provide high-precision image products,and has significant application value.
作者 薛武 王鹏 钟灵毓 XUE Wu;WANG Peng;ZHONG Ling-yu(Space Engineering University,Beijing 101416,China)
机构地区 航天工程大学
出处 《光学精密工程》 EI CAS CSCD 北大核心 2021年第12期2924-2934,共11页 Optics and Precision Engineering
基金 航天工程大学青年创新基金(2019)。
关键词 垂轨环扫 严格成像模型 粗纠正 影像匹配 精纠正 circular scanning perpendicular to the orbit strict imaging model coarse correction image matching accurate correction
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