Robust Particle Swarm Optimization of RFMs for High-Resolution Satellite Images Based on K-Fold Cross-Validation

Abstract

Rational function model (RFM) is one of the most popular methods of geometrically correcting high-resolution satellite images (HRSIs). This model encounters overparameterization problem due to the existence of highly correlated RFM coefficients, namely, rational polynomial coefficients (RPCs). Recently, a number of methods have been proposed based on particle swarm optimization (PSO) to find the optimal RPCs. Although these algorithms are useful for determining the optimal RPCs, their results are strongly influenced by changes in both initial values and ground control points (GCPs) distribution. To address this problem, this study proposes a modified version of PSO based on the k-fold cross-validation, known as PSO-KFCV, which works well even in the presence of limited GCPs. To evaluate the performance of the proposed method, four different HRSIs were used. Our experimental results indicate that PSO-KFCV is indeed robust against the initial values and GCPs distribution. In addition, the experiments demonstrated that the proposed method led to significant improvement with respect to state-of-the-art meta-heuristic methods.