| Automatic Building Extraction Using A Decision Tree Object-Based Classification On Joint Use Of Aerial And Lidar Data |
| Paper ID : 1220-SMPR |
| Authors: |
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Rezvan GHasemi Nejad1, Parham Pahlavani *1, Behnaz Bigdeli2 1School of Surveying and Geospatial Engineering, College of Eng., University of Tehran, Tehran, Iran 2School of Civil Eng., Shahrood University of Technology, Shahrood, Iran. |
| Abstract: |
| Updating digital maps is a challenging task that has been considered for many years and the requirement of up-to-date urban maps is universal. One of the main procedures used in updating digital maps and spatial databases is building extraction which is an active research topic in remote sensing and object-based image analysis (OBIA). Since in building extraction field a full automatic system are not yet operational and cannot be implemented in a single step, experts are used to define classification rules based on a complex and subjective "trial-and-error" process. In this paper, a decision tree classification method called, C4.5, was adopted to construct an automatic model for building extraction based on the remote sensing data. In this metod, a set of rules was derived automatically then a rule-based classification is applied to the remote sensing data include aerial and lidar images. The results of experiments showed that the obtained rules have exceptional predictive performance. |
| Keywords: |
| Building Extraction, Object-based, C4.5, decision tree, classification, Remote Sensing |
| Status : Conditional Accept (Poster) |
