Knowledge Commons of Institute of Automation,CAS
Road Extraction Via Adpative Graph Cuts with Multiple Features | |
Cheng GL(程光亮)![]() ![]() ![]() | |
2014 | |
会议名称 | IEEE International Conference on Image Processing |
会议日期 | 2014年10月 |
会议地点 | 法国巴黎 |
摘要 | Accurate 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 |
关键词 | Road Extraction Adaptive Graph Cuts Multiple Spatially Contextual Feature |
语种 | 英语 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/14536 |
专题 | 多模态人工智能系统全国重点实验室_先进时空数据分析与学习 |
作者单位 | 中国科学院自动化研究所 |
第一作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Cheng GL,Wang Y,Zhu FY,et al. Road Extraction Via Adpative Graph Cuts with Multiple Features[C],2014. |
条目包含的文件 | ||||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
388-u5Df-151.pdf(2360KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 |
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