Classification of quickbird image with maximal mutual information feature selection and support vector machine

Bo Wu, Zhu-guo Xiong, Yun-zhi Chen, Yin-di Zhao
2009 Procedia Earth and Planetary Science  
This paper presents a method to select optimal feature subset from object-orientated image segmentation according to the maximal mutual information to improve classification accuracy of high spatial resolution imagery over urban area. The proposed method is a three-step classification routine that involves the integration of 1) image segmentation with eCoginition software, 2) feature selection by maximal mutual information criterion, and 3) support vector machine for classification. Experiment
more » ... s conducted on Quick-Bird image in Fuzhou city. Furthermore, the proposed method with the well known feature selection methods, namely Tabu greedy search algorithm and fisher discriminate analysis, are evaluated and compared. The experiment shows that the mean error ratio significantly decreases with feature selection. It also demonstrates that the proposed maximal mutual information feature selection with support vector machine classifier significantly outperforms the classification method accompanied with eCoginition platform in terms of Z test.
doi:10.1016/j.proeps.2009.09.179 fatcat:aks44ykj2nddpi4lkjtgmieg3q