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A Penalized Spline-Based Attitude Model for High-Resolution Satellite Imagery

机译：基于惩罚样条的高分辨率卫星影像姿态模型

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摘要

Attitude models play a prominent role in the geometric processing of high-resolution satellite imagery (HRSI). Because of the high accuracy of the matching algorithm, attitude oscillations can occur in HRSI. Various methods for correcting this attitude oscillation with parallax observations have been proposed. However, few researchers have attempted to model the oscillation from the attitude records or have taken noise into consideration. In this paper, a penalized spline-based attitude model is proposed, which can model the oscillation with piecewise and continuously differentiable polynomials and smooth out the attitude noise with a penalty function. The balance between the fitting accuracy and noise smoothing is controlled by a penalty parameter, which is estimated by generalized cross-validation. Given that the attitude error introduces distortions into sensor-corrected images, the band-to-band registration of multispectral images is used to validate the attitude model. Five multispectral data sets captured by ZiYuan-3 are used to demonstrate the effectiveness of the proposed method. Compared with third-degree polynomials and cubic spline interpolation, the penalized spline model delivers the best performance by limiting the misregistration caused by the attitude model to within 0.1 pixels.
机译：姿态模型在高分辨率卫星图像（HRSI）的几何处理中起着重要作用。由于匹配算法的高精度，HRSI中可能会发生姿态振荡。已经提出了用于通过视差观察来校正该姿态振荡的各种方法。但是，很少有研究人员试图从姿态记录中模拟振动或将噪声考虑在内。本文提出了一种基于惩罚样条的姿态模型，该模型可以利用分段和连续微分多项式对振动进行建模，并通过惩罚函数消除姿态噪声。拟合精度和噪声平滑之间的平衡由惩罚参数控制，该惩罚参数由广义交叉验证估算。考虑到姿态误差会在传感器校正后的图像中引入失真，因此使用多光谱图像的波段间配准来验证姿态模型。 ZiYuan-3捕获的五个多光谱数据集用于证明该方法的有效性。与三次多项式和三次样条插值相比，惩罚样条模型通过将姿态模型引起的重合误差限制在0.1个像素以内，从而提供了最佳性能。

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