Volume XLII-5
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-5, 85-89, 2018
https://doi.org/10.5194/isprs-archives-XLII-5-85-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-5, 85-89, 2018
https://doi.org/10.5194/isprs-archives-XLII-5-85-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  19 Nov 2018

19 Nov 2018

PARTICLE SWARM OPTIMIZATION BASED APPROACH TO ESTIMATE EPIPOLAR GEOMETRY FOR REMOTELY SENSED STEREO IMAGES

M. Mahato and S. Gedam M. Mahato and S. Gedam
  • Centre of Studies in Resources Engineering, Indian Institute of Technology Bombay, India

Keywords: Fundamental Matrix, Particle Swarm Optimization, Epipolar Geometry, Remote Sensing, Random Sample Consensus, Stereo Vision

Abstract. A novel particle swarm optimization based approach for the estimation of epipolar geometry for remotely sensed images is proposed and implemented in this work. In stereo vision, epipolar geometry is described using 3 × 3 fundamental matrix and is used as a validation tool to assess the accuracy of the stereo correspondences. The validation is performed by enforcing the geometrical constraint of stereo images on the two perspective projections of a point in the scene for finding inliers. In the proposed method, the steps of particle swarm optimization such as the initialization of the position and velocity of the particles, the objective function to compute the best position found by the swarm as well as by each particle experienced so far, the updating rule of velocity for the improvement of the position of each particle, is designed and implemented to estimate the fundamental matrix. To demonstrate the effectiveness of the proposed approach, the results are obtained on a pair of remotely sensed stereo image. A comparison of the result obtained using the proposed algorithm with RANSAC algorithm is carried out. The comparison shows that, the proposed method is effective to estimate robust fundamental matrix by giving improved number of inliers than RANSAC.