遥感道路的场景感知与分类检测  

Scene Perception and Classified Detection for Roads in Remote Sensing Images

作　　者：杨俊[1] 王润生[1] 

机构地区：[1]国防科学技术大学ATR国家重点实验室,长沙410073

出　　处：《计算机辅助设计与图形学学报》2007年第3期334-339,共6页Journal of Computer-Aided Design & Computer Graphics

摘　　要：基于人类视觉的感知组织和分类融合的基本原理,对遥感图像道路目标的感知模型进行了改进,改进模型分为像素、基元、结构和目标4个处理层次,比传统模型增加了道路场景的自动分类和多类型道路的整合连通2个子过程.基于该模型,提出了一种遥感图像道路检测算法,根据不同的道路场景定义了块状和线状2种不同的道路基元,并分别利用不同方法加以提取、度量和连接.2种基元的连接都采用了显著度驱动的层次性搜索策略.所有连接的路段最终都以中心主线的形式统一描述,并被整合为平滑的全局道路曲线.实验结果表明,该算法能从实际的卫星图像中检测出多种类型的道路,并具有较好的适用能力和有效性.This paper presents an improved perception model for roads in remote sensing images based on the principles of perceptual organization and classification fusion in HVS. The model consists of four levels： pixels, elements, structures and objects, and two additional sub-processes are incorporated compared with the traditional one： Automatic classification of road scenes and global integration of multiform roads. Based on the model, a novel algorithm for detecting roads from remote sensing images is also proposed, in which two types of road primitives, namely blob-primitive and line-primitive are defined, measured, extracted and linked using different methods for dissimilar road scenes. A hierarchical search strategy driven by saliency measurement is adopted in both linking processes. Finally, all the linked road segments are normalized with center-main lines and integrated into global smooth road curves. Experimental results show that the algorithm can detect multiform roads from real satellite images with high adaptability and reliability.

关 键 词：道路检测 感知组织 分类 整合 道路基元 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]