A NOVEL POINT-MATCHING ALGORITHM BASED ON FAST SAMPLE CONSENSUS FOR IMAGE REGISTRATION

Domain: DIP Project.

Robustness and accuracy are the two main challenging problems in feature-based remote sensing image registration. In this letter, a novel point-matching algorithm is proposed. An improved random sample consensus (RANSAC) algorithm called fast sample consensus (FSC) is proposed. It divides the data set in RANSAC into two parts: the sample set and the consensus set. Sample set has high correct rate and consensus set has a large number of correct matches. An iterative method is put forward to increase the number of correct correspondences. A set of measures has been used to evaluate the registration result. The performance of the proposed method is validated on the evaluation of these measures and the mosaic images. FSC can get more correct matches than RANSAC in less number of iterations, iterative selection of correct matches algorithm and removal of the imprecise points algorithm effectively increase the accuracy of the result. Extensive experimental studies compared with three state-of-the-art methods prove that the proposed algorithm is robust and accurate.

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