Skip to Main content Skip to Navigation
New interface
Journal articles

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

Régis Perrier 1 Élise Arnaud 1 Peter Sturm 1 Mathias Ortner 2 
1 STEEP - Sustainability transition, environment, economy and local policy
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann
Abstract : Pushbroom cameras are widely used for earth observation applications. This sensor acquires 1-D images over time and uses the straight motion of the satellite to sweep out a region of space and build a 2-D 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 4 satellite datasets.
Document type :
Journal articles
Complete list of metadata
Contributor : Peter Sturm Connect in order to contact the contributor
Submitted on : Wednesday, December 10, 2014 - 1:58:10 PM
Last modification on : Thursday, April 28, 2022 - 12:34:02 AM




Régis Perrier, Élise Arnaud, Peter Sturm, Mathias Ortner. Estimation of an Observation Satellite’s Attitude using Multimodal Pushbroom Cameras. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37 (5), pp.987-1000. ⟨10.1109/TPAMI.2014.2360394⟩. ⟨hal-01093238⟩



Record views