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Estimation of an Observation Satellite’s Attitude Using Multimodal Pushbroom Cameras

机译:使用多模式推扫式照相机估算观察卫星的姿态

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Pushbroom cameras are widely used for earth observation applications. This sensor acquires 1D images over time and uses the straight motion of the satellite to sweep out a region of space and build a 2D image. The stability of the satellite is critical during the pushbroom acquisition process. Therefore its attitude is assumed to be constant over time. However, the recent manufacture of smaller and lighter satellites to reduce launching cost has weakened this assumption. Small oscillations of the satellite’s attitude can result in noticeable warps in images, and geolocation information is lost as the satellite does not capture what it ought to. Current solutions use inertial sensors to control the attitude and correct the images, but they are costly and of limited precision. As the warped images do contain information about attitude variations, we suggest using image registration to estimate them. We exploit the geometry of the focal plane and the stationary nature of the disturbances to recover undistorted images. We embed the estimation in a Bayesian framework where image registration, a prior on attitude variations and a radiometric correction model are fused to retrieve the motion of the satellite. We illustrate the performance of our algorithm on four satellite datasets.
机译:手推式摄像机广泛用于地球观测应用。该传感器随时间获取一维图像,并使用卫星的直线运动扫出空间区域并生成二维图像。在推扫帚采集过程中,卫星的稳定性至关重要。因此,假定其态度随时间变化是恒定的。但是,最近为减少发射成本而制造的更小,更轻的卫星削弱了这一假设。卫星姿态的微小波动会导致图像出现明显的翘曲,并且由于卫星无法捕获其应有的图像,因此丢失了地理位置信息。当前的解决方案使用惯性传感器来控制姿态并校正图像,但是它们昂贵且精度有限。由于扭曲的图像确实包含有关姿态变化的信息,因此我们建议使用图像配准来估计它们。我们利用焦平面的几何形状和干扰的平稳特性来恢复未失真的图像。我们将估计值嵌入贝叶斯框架中,在该框架中将图像配准,先验的姿态变化和辐射校正模型融合在一起以检索卫星的运动。我们说明了我们的算法在四个卫星数据集上的性能。

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