| 基于P-N学习的高分遥感影像道路半自动提取方法 |
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| 引用本文： | 陈光,眭海刚,涂继辉,宋志娜. 基于P-N学习的高分遥感影像道路半自动提取方法[J]. 武汉大学学报(信息科学版), 2017, 42(6): 775-781. DOI: 10.13203/j.whugis20140999 |
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| 作者姓名： | 陈光 眭海刚 涂继辉 宋志娜 |
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| 作者单位： | 1.武汉大学测绘遥感信息工程国家重点实验室, 湖北 武汉, 430079 |
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| 基金项目： | 国家973计划2012CB719906 |
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| 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。
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| 关 键 词： | 高分辨率 道路提取 模板匹配 P-N学习 |
| 收稿时间： | 2015-07-26 |
Semi-automatic Road Extraction Method from High Resolution Remote Sensing Images Based on P-N Learning |
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| Affiliation: | 1.State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China2.Chongqing Surveying Institute, Chongqing 400020, China3.School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China |
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| Abstract: | 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. |
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