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Road Extraction Via Adpative Graph Cuts with Multiple Features
Cheng GL(程光亮); Wang Y(王颖); Zhu FY(朱飞云); Pan CH(潘春洪)
Conference NameIEEE International Conference on Image Processing
Conference Date2014年10月
Conference Place法国巴黎
AbstractAccurate road extraction from complex backgrounds plays a fundamental role in a wide range of remote sensing applications. There are two shortcomings for the existing methods: 1) Most of them ignore the spatially contextual information inherent in images; 2) Few existing methods show robustness to the occlusions of cars or trees. To address these two problems, we propose a novel approach via adaptive graph cuts with multiple features. Specifically, for the former problem, we apply multiple features (spectral feature, spatial feature and gradient feature) to obtain not only the spectral characteristic but also the spatially contextual feature. In this way, the structural information of road network can be effectively captured. For the latter, adaptive graph cuts based algorithm is adopted. These two schemes show better performance than state-of-the-art methods under the conditions of occlusions. Experiments on 25 images indicate the validity and effectiveness of our method by comparing with state-of-the-art approaches
KeywordRoad Extraction Adaptive Graph Cuts Multiple Spatially Contextual Feature
Document Type会议论文
Recommended Citation
GB/T 7714
Cheng GL,Wang Y,Zhu FY,et al. Road Extraction Via Adpative Graph Cuts with Multiple Features[C],2014.
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