In recent years, many researches focused on the supervised machine learning classification methods using Lidar and remotely sensed image to provide buildings, trees, roads, and grass categories for urban ground feature classification. First, this research performed urban ground feature classification based on true color aerial imagey and Lidar Intensity. Second, Lidar derived normalized DSM (nDSM) was added to the classification. Finally, the concept of height level rules was applied. This research utilized two-level height rule-based classification exteneded from three-level height rule-based classification (Huang, 2007). It is obvious to observ the overlap for the roads and houses, and grass and trees in the feature space plot where result in the classification confusion. These confusions can be resolved by fusion the height information. After comparing classification accuracy, the two-level height is better than three-level height classification scheme.
This research proposed hybrid classification method based on Maximum likelihood classification (MLC) and two-level height rules. This method reveals the role of height information in urban ground feature classification. The height level rules were also applied to other supervised classification method such as Back-Propagation Network (BPN) and Support Vector Machine (SVM). The classification results show that the accuracy of hybrid method is better than the orgional classification method. However, the time required to look for the classification parameters for BPN and SVM is greater than MLC but only can derived considerable results. Therefore, the hybrid classification method based on MLC is better than other two methods.
| Identifer | oai:union.ndltd.org:NSYSU/oai:NSYSU:etd-0727108-100826 |
| Date | 27 July 2008 |
| Creators | Ciou, Jhih-yuan |
| Contributors | Liang-huei Lee, Shiahn-wern Shyue, Tian-yuan Shih, Ming-jer Huang |
| Publisher | NSYSU |
| Source Sets | NSYSU Electronic Thesis and Dissertation Archive |
| Language | Cholon |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0727108-100826 |
| Rights | unrestricted, Copyright information available at source archive |
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