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在遥感影像几何精校正过程中,无论是通过人工选择还是特征匹配方法选择的控制点对都会随机发生误匹配的现象,这将大大影响几何精校正的精度。针对这一问题,本文利用压缩感知理论,利用误匹配控制点在所有控制点对中的稀疏性,实现了几何校正模型参数的抗差估计,提高了几何校正结果的精度。对卫星遥感影像的几何精校正的试验结果表明,基于压缩感知的遥感影像几何精校正方法能够有效克服误匹配控制点的影响。
In the course of geometric rectification of remote sensing images, the control point pairs selected by manual selection or feature matching are randomly mismatched, which will greatly affect the accuracy of geometric precision correction. In order to solve this problem, this paper uses the compressive sensing theory to make use of the sparsity of mismatched control points in all control point pairs, and realizes the robust estimation of geometrical correction model parameters and improves the accuracy of geometric correction results. The experimental results of geometric refinement of satellite remote sensing images show that the geometric refinement of remote sensing images based on compressive sensing can effectively overcome the influence of mismatch control points.