摘要
基于模板匹配的道路跟踪是半自动提取道路的主要方法。然而场景中地物干扰和道路宽度的变化降低了模板匹配的稳定性;另外,道路跟踪失败后缺乏重检测机制,使得道路提取过程中人机交互频繁。针对以上问题,提出了一种基于P-N(positive-negative)学习的高分遥感影像道路半自动提取方法。该方法由道路跟踪、检测和学习构成,关键是采用了P-N学习的策略迭代的训练分类器,通过纠正违反结构约束的样本分类结果来提高分类器性能。实验使用了不同场景下的城区高分遥感影像,与经典的模板匹配和在线学习的道路跟踪方法进行了比较。实验结果表明该方法在道路提取的精度和稳定性方面均有提升。
The road tracking method based on template matching is one major semi-automatic road extraction method. However, template matching is sensitive to complexity of road scenes and variance in road width. In addition, road extraction requires frequent human-computer interaction while road tracking encounters failure without a mechanism for re-detection. To solve these problems, one semi-automatic road extraction method using high resolution remote sensing image based on P-N learning is proposed. It consists of road tracking, detecting and learning. In order to improve the stability of road detection, we train a classifier with an iterative P-N learning strategy. The performance of classifier is improved by correcting sample labeling under structural constraints. In experiments, the proposed method and three classical methods are tested on high-resolution remote sensing images of different scenes. Comparitive results show proposed method' improves precision and stability of road extraction.
出处
《武汉大学学报(信息科学版)》
EI
CSCD
北大核心
2017年第6期775-781,共7页
Geomatics and Information Science of Wuhan University
基金
国家973计划(2012CB719906)
高分辨率对地观测系统重大专项~~
关键词
高分辨率
道路提取
模板匹配
P-N学习
high resolution
road extraction
template matching
P-N learning