Sensor Measurements and Image Registration Fusion to Retrieve Variations of Satellite Attitude

Perrier, Regis; Arnaud, Élise; Sturm, Peter; Ortner, Mathias

doi:10.1007/978-3-642-19282-1_29

Cited by 1 publication

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“…If it is relatively easy to project the line of sight of the pushbroom camera on the ground given a DEM, the inverse operation which computes the location of a point on the pushbroom image given its spatial coordinates on the ground is insoluble without strong hypotheses like in [31]. Function W should be seen as an operator which explains how pixels move in an image when the satellite is not steady during the acquisition process.…”

Section: Understanding Image Registrationmentioning

confidence: 99%

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

Perrier

Arnaud

Sturm

et al. 2015

IEEE Trans. Pattern Anal. Mach. Intell.

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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 overtime. 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.

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Section: Understanding Image Registrationmentioning

confidence: 99%