Remote sensing images obtained from onboard linear array cameras suffer from geometric disturbances in the presence of attitude jitter from a satellite platform. Thus, platform jitter estimation is essential for improving satellite stability and enhancing remote sensing image quality. In this article, a novel integration framework is designed to estimate attitude fluctuation, which combines multispectral advanced spaceborne thermal emission and reflection/short-wave-infrared (ASTER/SWIR) data and blurred star images from onboard optical sensors. First, a multilevel XGBoost algorithm is applied to learn the nonlinear relationship between the star blurring and instantaneous jitter displacement. An image registration method and a deconvolutional process are then introduced to remove the satellite jitter from ASTER/SWIR data. Next, an augmented $H$$_\infty$ filter is proposed to fuse and estimate the satellite jitter from two algorithms. Simulating experiment results indicate that the jitter estimation error of the proposed integration framework is reduced by 40%. Compared with the existing jitter estimation methods only based on remote sensing image processing, our strategy has better robustness and accuracy, especially on extreme ground scenes.

中文翻译：

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基于ASTER/SWIR和模糊星图的卫星抖动估计增强$H_\infty$滤波器

在卫星平台存在姿态抖动的情况下，从机载线阵相机获得的遥感图像会受到几何干扰。因此，平台抖动估计对于提高卫星稳定性和提高遥感图像质量至关重要。在本文中，设计了一种新颖的集成框架来估计姿态波动，该框架结合了多光谱先进星载热发射和反射/短波红外 (ASTER/SWIR) 数据以及来自机载光学传感器的模糊恒星图像。首先，应用多级 XGBoost 算法来学习星形模糊和瞬时抖动位移之间的非线性关系。然后引入图像配准方法和去卷积过程以从 ASTER/SWIR 数据中去除卫星抖动。下一个，提出了一个增强的 $H$$_\infty$ 滤波器来融合和估计来自两种算法的卫星抖动。仿真实验结果表明，所提出的集成框架的抖动估计误差降低了40%。与现有仅基于遥感图像处理的抖动估计方法相比，我们的策略具有更好的鲁棒性和准确性，尤其是在极端地面场景下。