Developing a New Method in Object Based Classification to Updating Large Scale Maps with Emphasis on Building Feature

According to the cities expansion, updating urban maps for urban planning is important and its effectiveness is depend on the information extraction / change detection accuracy. Information extraction methods are divided into two groups, including Pixel-Based (PB) and Object-Based (OB). OB analysis has overcome the limitations of PB analysis (producing salt-pepper results and features with holes). In the information extraction by SVM classification in complex urban areas, using various features was suggested to improve accuracy result. Also, in SVM, it is necessary to determine the values of the model parameters. In most of the previous OB research, the two important steps were determined by trial and error or based on an expert knowledge. The necessity of selecting independent features and determining the optimal values of SVM parameters, with the aim of minimizing the maximum user interaction, have resulted in proposing a novel method with a relatively high automation level based on SVM simultaneously optimization with meta-heuristic algorithms for large scale updating maps in  high spatial resolution and elevation data. Semi-automatic selection of train/test samples also has increased the automation level of the updating process. Therefore, according to the effect of information extraction on the updating results, the proposed method is trying to improve this step results. The results of the proposed method had no salt-pepper results in comparison with PB analysis. Also, the time processing of the proposed method in optimization and classification steps had been decreased. Finally, the results of change detection map obtained from the proposed method led to 9% and 5% improvement in comparison with other methods in changed class.