Road network extraction and reconstruction using high resolution satellite imagery
| Autor: | Hong-Yi Huang, 黃弘毅 |
|---|---|
| Rok vydání: | 2019 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 107 Automatic road extraction from remote-sensing imagery plays an important role in many applications. In general, road extraction from remote-sensing imagery can be considered as a classification process in which pixels are divided into road classes and others. This study develops a systematic procedure using high-resolution satellite imagery to extract and reconstruct road network in urban areas with a road width greater than 8 meters. The developed procedure for extracting roads and reconstructing the network can be divided into three steps. The first step; obtaining texture parameters, including contrast, entropy and homogeneity of each pixel using gray level co- occurrence matrix (GLCM). Afterwards, the support vector machine (SVM) is used as a classifier to identify road pixels. Finally, linear feature detection of the road network with radon transform is performed. This study uses high resolution Pleiades satellite imagery which covers an area of 8 km2 of Taipei city, Taiwan. Experiment results show that the proposed procedure and method can achieve an overall accuracy of 93.11% with a kappa coefficient of 89.20% and completeness over 85%. The results indicate the proposed method is efficient to extract road network from high resolution satellite images. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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