摘要
为更有效地将空间观测技术应用于城市地理信息系统等领域,提出了一种新的基于遥感图像的城市道路自动测绘方法。该方法通过构建对象网络来表达图像结构,获取客观的处理单元。在此基础上,针对感兴趣特征,利用无监督学习来综合分析遥感图像中道路目标的各类可视及非可视化信息,快速标记并定位目标区域。方法中还结合上下文信息进行空间平滑处理,大大消除了噪声、遮挡等影响。矢量标绘后可以量测得到城市道路的准确轮廓及相关参数。实验表明,该方法准确率高、鲁棒性好,适用于绝大多数高分辨率城市遥感图像中道路目标的自动测绘,在地理信息系统和数字城市系统建设中具有较大的实用价值。
For the purpose of applying the spatial observation technology into the urban geographical information system, a new method based on remote sensing images is proposed to detect and map urban roads automatically. It builds a hierarchical objects network to organize image structure, getting precise processing units. Then the unsupervised learning integrating salient features is performed to analyze the explicit and implicit information, and to label the road targets in remote sensing images efficiently. It also applies spatial smoothing which incorporates contextual information to eliminate the adverse effects caused by background disturbance, occlusion and so on. After vectorization procedure, the contours and relative parameters of roads can be given. Experiments demonstrate that the proposed method achieves high exactness and robustness when detecting and mapping manifold roads in most highresolution remote sensing images, and it is valuable for the urban information system and digital city-system construction.
出处
《光学学报》
EI
CAS
CSCD
北大核心
2009年第1期86-92,共7页
Acta Optica Sinica
基金
国家自然科学基金(40871209)
国家863计划(2006AA12Z149)
中国科学院电子学研究所青年创新基金资助项目
关键词
图像处理
目标识别
道路测绘
城市遥感图像
无监督学习
基于对象
image processing
objects detection
road mapping
urban remote sensing images
unsupervised learning
objects-based
