Efficient method for outlier removal in SAR image matching based on epipolar geometry

Abstract

A method is proposed for removing falsely matched points after sparse matching in synthetic aperture radar (SAR) images. This method uses epipolar geometry as well as random sample consensus (RANSAC) to automatically remove false matches (outliers) among all points. The method randomly selects at least four points among all matched points. Then, using a bilinear equation, a generic relation is established between the pixel coordinates of a reference point in the master image and the parameters of the corresponding epipolar line in the slave image. Therefore, it is possible to calculate the epipolar line for all points using the bilinear function and then evaluate the accuracy of the matched points. Moreover, the proposed method is compared with the method that uses the RANSAC with an affine function, which is a common method for removing outliers. Here, the output of the scale-invariant feature transform algorithm is used to conduct the experiments. Furthermore, the SAR image pairs from southern Iran are utilised to evaluate the performance of the method. Experiments are conducted on TerraSAR-X images, and the results demonstrate that the proposed method has high reliability for removing false matches.