Gemetic Alogorithm 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.
Genetic algorithm Svm Grid algorithm Object-based High spatial resolution image
Mengmeng Li Xiaocheng Zhou Xiaoqin Wang Bo Wu
Spatial information Research Center of Fujian Province, Fuzhou University Fuzhou 350002,China
国际会议
福州
英文
348-352
2011-06-29（万方平台首次上网日期，不代表论文的发表时间）