An Improved Binary Particle Swarm Optimization of RFM’s for ALSAT2 Imagery
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Date
2021-05-25
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Publisher
University of Oum El Bouaghi
Abstract
Rational function model (RFM) is commonly used in photogrammetric and remote sensing applications because it 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. This paper proposes a new binary particle swarm optimization algorithm to surmount the issue of over parameterization and find the optimum combination of RPCs for the RFM by adding a new transfer function in binary PSO in order to increase the convergence speed and avoid the local minimum phenomenon. The results showed that the proposed method is compatible with different types of RFM, more stable, and gives higher accuracy than the traditional binary PSO.
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Keywords
Rational function model; particle swarm optimization; Ortho-rectification; meta-heuristic; high-resolution satellite images