Genetic algorithm optimized SVM in object-based classification of quickbird imagery

This paper presents a genetic algorithm (GA) approach for parameters optimization of support vector machine, which is used for the object-oriented classification of high spatial resolution images over urban area. The proposed method is a three-step routine involves the integration of 1) image segmentation, 2) GA-based parameter optimization of Support vector machine (SVM), and 3) objected-based classification. Experiments conducted on multi-spectral Quick-Bird image fused with panchromatic image in Fuzhou city. In addition, a traditional parameter searching method, Grid algorithm, was investigated to evaluate the effectiveness of the proposed approach. The results show that our proposed GA-based approach significantly outperforms the Grid algorithm both in terms of classification accuracy and time efficiency.