Image ortho-rectification is a standard process in remote sensing for correcting the geometric distortions and relief displacement errors introduced by the payload system during the imaging time. It requires a precise rigorous sensor model or rational function models which are refined using well-distributed ground control points. The Rational function model (RFM) is commonly used because of its simplest model and does not need sensor parameters. Therefore, the RFM terms or also rational polynomial coefficients (RPCs) have no physical significance but depends on many ground control points (GCPs) that make the model prone to the over parameterization problem. The application of meta-heuristic algorithms is suited for RFM optimization. This paper proposes a binary particle swarm optimization BPSO to surmount the issue of
over-parameterization and find the optimum combination of RPCs for the RFM by adding a new transfer function. The algorithm is applied to the ALSAT2 images and the results showed the effectiveness and the accuracy of BPSO over the traditional binary literature methods. Furthermore, a hybrid optimization technique is introduced that blends the BPSO concept by adding the genetic operations such as crossover and mutation in order to increase the convergence speed and avoid the local optimum phenomenon. The proposed method gives a better result than the suggested one.
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