Robust Particle Swarm Optimization of RFMs for High-Resolution Satellite Images Based on K-Fold Cross-Validation
Autor: | Alireza Amiri-Simkooei, Saeid Gholinejad, Amin Alizadeh Naeini |
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Rok vydání: | 2019 |
Předmět: |
Atmospheric Science
010504 meteorology & atmospheric sciences Fold (higher-order function) 0211 other engineering and technologies High resolution Particle swarm optimization 02 engineering and technology Rational function Rational polynomial 01 natural sciences Cross-validation Distribution (mathematics) Satellite Computers in Earth Sciences Algorithm 021101 geological & geomatics engineering 0105 earth and related environmental sciences Mathematics |
Zdroj: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 12:2594-2599 |
ISSN: | 2151-1535 1939-1404 |
Popis: | 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. |
Databáze: | OpenAIRE |
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