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

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.

[1]  I. Du,et al.  Direct Methods , 1998 .

[2]  A. Jalobeanu Predicting spatial uncertainties in stereo photogrammetry: achievements and intrinsic limitations , 2011 .

[3]  Shmuel Peleg,et al.  Multi-sensor super-resolution , 2002, Sixth IEEE Workshop on Applications of Computer Vision, 2002. (WACV 2002). Proceedings..

[4]  Daniela Poli GENERAL MODEL FOR AIRBORNE AND SPACEBORNE LINEAR ARRAY SENSORS , 2002 .

[5]  Carlo Tomasi,et al.  Good features to track , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[6]  Paul J. Walmsley,et al.  Bayesian Approaches to Multi-Sensor Data Fusion , 1999 .

[7]  A. Gruen,et al.  Sensor Modeling for Aerial Triangulation with Three-Line-Scanner (TLS) Imagery , 2003 .

[8]  Jordi Inglada,et al.  On the possibility of automatic multisensor image registration , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[9]  Rajiv Gupta,et al.  Linear Pushbroom Cameras , 1994, ECCV.

[10]  N. Haala,et al.  DIRECT GEOREFERENCING USING GPS/INERTIAL EXTERIOR ORIENTATIONS FOR PHOTOGRAMMETRIC APPLICATIONS , 2000 .

[11]  Paul Rademacher,et al.  Multiple-center-of-projection images , 1998, SIGGRAPH.

[12]  Gerd Hirzinger,et al.  Stereo Vision Based Reconstruction of Huge Urban Areas from an Airborne Pushbroom Camera (HRSC) , 2005, DAGM-Symposium.

[13]  Jan Flusser,et al.  Image registration methods: a survey , 2003, Image Vis. Comput..

[14]  Peter F. Sturm,et al.  Sensor Measurements and Image Registration Fusion to Retrieve Variations of Satellite Attitude , 2010, ACCV.

[15]  Richard Szeliski,et al.  Computer Vision - Algorithms and Applications , 2011, Texts in Computer Science.

[16]  Peter F. Sturm,et al.  Estimating satellite attitude from pushbroom sensors , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[17]  Richard Bowden,et al.  Mutual Information for Lucas-Kanade Tracking (MILK): An Inverse Compositional Formulation , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  Peter F. Sturm,et al.  Plane-Based Calibration for Linear Cameras , 2008, International Journal of Computer Vision.

[19]  Peyman Milanfar,et al.  Fundamental performance limits in image registration , 2003, IEEE Transactions on Image Processing.

[20]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  P. Anandan,et al.  Robust multi-sensor image alignment , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[22]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

[23]  H. Akaike A new look at the statistical model identification , 1974 .

[24]  David J. C. MacKay,et al.  Bayesian Interpolation , 1992, Neural Computation.

[25]  Takeo Kanade,et al.  An Iterative Image Registration Technique with an Application to Stereo Vision , 1981, IJCAI.

[26]  Nassir Navab,et al.  Pixel-Based Hyperparameter Selection for Feature-Based Image Registration , 2010, VMV.

[27]  Akira Iwasaki Detection and Estimation Satellite Attitude Jitter Using Remote Sensing Imagery , 2011 .

[28]  J. Makhoul,et al.  Linear prediction: A tutorial review , 1975, Proceedings of the IEEE.

[29]  N. Ayache,et al.  Multimodal Brain Warping Using the Demons Algorithm and Adaptative Intensity Corrections , 1999 .

[30]  Christophe Renard,et al.  Design of the high resolution optical instrument for the Pleiades HR Earth observation satellites , 2017, International Conference on Space Optics.

[31]  Simon Baker,et al.  Lucas-Kanade 20 Years On: A Unifying Framework , 2004, International Journal of Computer Vision.

[32]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[33]  David J. C. MacKay,et al.  Information Theory, Inference, and Learning Algorithms , 2004, IEEE Transactions on Information Theory.

[34]  D. Greslou,et al.  PROCESS LINE FOR GEOMETRICAL IMAGE CORRECTION OF DISRUPTIVE MICROVIBRATIONS , 2008 .

[35]  R. Gupta Camera Estimation for Orbiting Pushbrooms , 1995 .

[36]  Josiane Zerubia,et al.  Hyperparameter estimation for satellite image restoration using a MCMC maximum-likelihood method , 2002, Pattern Recognit..

[37]  Nicholas Ayache,et al.  Three-dimensional multimodal brain warping using the Demons algorithm and adaptive intensity corrections , 2001, IEEE Transactions on Medical Imaging.

[38]  A. Bartoli,et al.  A Generic Rolling Shutter Camera Model and its Application to Dynamic Pose Estimation , 2010 .

[39]  Nicholas Ayache,et al.  Towards a Better Comprehension of Similarity Measures Used in Medical Image Registration , 1999, MICCAI.

[40]  Carl E. Rasmussen,et al.  Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.