| Road network extraction in classified SAR images using genetic algorithm |
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| 作者姓名： | 肖志强 鲍光淑 蒋晓确 |
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| 作者单位： | SchoolofInfo--PhysicsandGeomaticsEngineering,CentralSouthUniversity,Changsha410083,China |
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| 摘 要： | Due to the complicated background of objectives and speckle noise, it is almost impossible to extract roads directly from original synthetic aperture radar(SAR) images. A method is proposed for extraction of road net-work from high-resolution SAR image. Firstly, fuzzy C means is used to classify the filtered SAR image unsupervis-edly, and the road pixels are isolated from the image to simplify the extraction of road network. Secondly, according to the features of roads and the membership of pixels to roads, a road model is constructed, which can reduce the extraction of road network to searching globally optimization continuous curves which pass some seed points. Final-ly, regarding the curves as individuals and coding a chromosome using integer code of variance relative to coordi-nates, the genetic operations are used to search global optimization roads. The experimental results show that the al-gorithm can effectively extract road network from high-resolution SAR images.
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| 关 键 词： | 遗传运算法则 路网萃取 安全分析报告 孔径雷达 图象处理 |
| 收稿时间： | 1 July 2003 |
| 修稿时间： | 5 February 2004 |
Road network extraction in classified SAR images using genetic algorithm |
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| Xiao Zhi-qiang , Bao Guang-shu and Jiang Xiao-que.Road network extraction in classified SAR images using genetic algorithm[J].Journal of Central South University of Technology,2004,11(2):180-184. |
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| Authors: | Xiao Zhi-qiang Bao Guang-shu and Jiang Xiao-que |
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| Affiliation: | (1) School of Info-Physics and Geomatics Engineering, Central South University, 410083 Changsha, China |
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| Abstract: | Due to the complicated background of objectives and speckle noise, it is almost impossible to extract roads directly from original synthetic aperture radar(SAR) images. A method is proposed for extraction of road network from high-resolution SAR image. Firstly, fuzzy C means is used to classify the filtered SAR image unsupervisedly, and the road pixels are isolated from the image to simplify the extraction of road network. Secondly, according to the features of roads and the membership of pixels to roads, a road model is constructed, which can reduce the extraction of road network to searching globally optimization continuous curves which pass some seed points. Finally, regarding the curves as individuals and coding a chromosome using integer code of variance relative to coordinates, the genetic operations are used to search global optimization roads. The experimental results show that the algorithm can effectively extract road network from high-resolution SAR images. |
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| Keywords: | genetic algorithm road network extraction SAR image fuzzy C means |
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