Linear pushbroom cameras

Modeling and analyzing pushbroom sensors commonly used in satellite imagery is difficult and computationally intensive due to the motion of an orbiting satellite with respect to the rotating Earth, and the nonlinearity of the mathematical model involving orbital dynamics. In this paper, a simplified model of a pushbroom sensor (the linear pushbroom model) is introduced. It has the advantage of computational simplicity while at the same time giving very accurate results compared with the full orbiting pushbroom model. Besides remote sensing, the linear pushbroom model is also useful in many other imaging applications. Simple noniterative methods are given for solving the major standard photogrammetric problems for the linear pushbroom model: computation of the model parameters from ground-control points; determination of relative model parameters from image correspondences between two images; and scene reconstruction given image correspondences and ground-control points. The linear pushbroom model leads to theoretical insights that are approximately valid for the full model as well. The epipolar geometry of linear pushbroom cameras is investigated and shown to be totally different from that of a perspective camera. Nevertheless, a matrix analogous to the fundamental matrix of perspective cameras is shown to exist for linear pushbroom sensors. From this it is shown that a scene is determined up to an affine transformation from two views with linear pushbroom cameras.

[1]  O. Faugeras Three-dimensional computer vision: a geometric viewpoint , 1993 .

[2]  Richard I. Hartley,et al.  Estimation of Relative Camera Positions for Uncalibrated Cameras , 1992, ECCV.

[3]  Minas E. Spetsakis,et al.  Optimal Visual Motion Estimation: A Note , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Joseph L. Mundy,et al.  X-ray metrology for quality assurance , 1994, Proceedings of the 1994 IEEE International Conference on Robotics and Automation.

[5]  Richard I. Hartley,et al.  In defence of the 8-point algorithm , 1995, Proceedings of IEEE International Conference on Computer Vision.

[6]  T. Strat Recovering the camera parameters from a transformation matrix , 1987 .

[7]  Olivier D. Faugeras,et al.  On the geometry and algebra of the point and line correspondences between N images , 1995, Proceedings of IEEE International Conference on Computer Vision.

[8]  Olivier D. Faugeras,et al.  What can be seen in three dimensions with an uncalibrated stereo rig , 1992, ECCV.

[9]  O. D. Faugeras,et al.  Camera Self-Calibration: Theory and Experiments , 1992, ECCV.

[10]  Richard Hartley An object-oriented approach to scene reconstruction , 1996, 1996 IEEE International Conference on Systems, Man and Cybernetics. Information Intelligence and Systems (Cat. No.96CH35929).

[11]  Thomas S. Huang,et al.  Uniqueness and Estimation of Three-Dimensional Motion Parameters of Rigid Objects with Curved Surfaces , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Gunnar Sparr Depth computations from polyhedral images , 1992, Image Vis. Comput..

[13]  Stephen Wolfram,et al.  Mathematica: a system for doing mathematics by computer (2nd ed.) , 1991 .

[14]  Sundaram Ganapathy,et al.  Decomposition of transformation matrices for robot vision , 1984, Pattern Recognit. Lett..

[15]  Rajiv Gupta,et al.  Stereo from uncalibrated cameras , 1992, Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[16]  Richard I. Hartley,et al.  Euclidean Reconstruction from Uncalibrated Views , 1993, Applications of Invariance in Computer Vision.

[17]  H. C. Longuet-Higgins,et al.  A computer algorithm for reconstructing a scene from two projections , 1981, Nature.

[18]  J. G. Semple,et al.  Algebraic Projective Geometry , 1953 .

[19]  Rajiv Gupta,et al.  Computing matched-epipolar projections , 1993, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[20]  R. Fateman,et al.  A System for Doing Mathematics by Computer. , 1992 .

[21]  Ivan E. Sutherland,et al.  Three-dimensional data input by tablet , 1974 .

[22]  Marsha Jo Hannah,et al.  Bootstrap Stereo , 1980, AAAI.

[23]  David A. Forsyth,et al.  Canonical Frames for Planar Object Recognition , 1992, ECCV.

[24]  Narendra Ahuja,et al.  Motion and Structure From Two Perspective Views: Algorithms, Error Analysis, and Error Estimation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[25]  Berthold K. P. Horn Relative orientation revisited , 1991 .