This study presents an integrated framework for the automatic generation of the photorealistic 3-d building models from satellite and ground-level imagery. First, the 2-d building patches and the corresponding footprints are extracted from a high resolution imagery using an adaptive fuzzy-genetic
algorithm approach. Next, the photorealistic facade textures are automatically extracted from the single ground-level building images using a developed approach, which includes facade image extraction, rectification, and occlusion removal. Finally, the textured 3-d building models are generated
automatically by mapping the corresponding textures onto the facades of the models.
The developed 2-d building extraction and delineation approach was implemented on a selected urban area of the Batikent district of Ankara, Turkey. The building regions were extracted with an approximate detection rate of 93%. Moreover, the overall delineation accuracy was computed to be 3.9 meters. The developed concept for facade image extraction was tested on two distinct datasets. The facade image extraction accuracies were computed to be 82% and 81% for the Batikent and eTrims datasets, respectively. As to rectification results, 60% and 80% of the facade images
provided errors under ten pixels for the Batikent and eTrims datasets, respectively. In the evaluation of occlusion removal, the average scores were computed to be 2.58 and 2.28 for the Batikent and eTrims datasets, respectively. The scores are ranked between 1 (Excellent) to 6 (Unusable).
The modeling of the total 110 single buildings with the photorealistic textures took about 50 minutes of processor running time and yielded a satisfactory level of accuracy.
| Identifer | oai:union.ndltd.org:METU/oai:etd.lib.metu.edu.tr:http://etd.lib.metu.edu.tr/upload/12613131/index.pdf |
| Date | 01 April 2011 |
| Creators | Sumer, Emre |
| Contributors | Atalay, Volkan |
| Publisher | METU |
| Source Sets | Middle East Technical Univ. |
| Language | English |
| Detected Language | English |
| Type | Ph.D. Thesis |
| Format | text/pdf |
| Rights | To liberate the content for public access |
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