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- 2017
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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Abstract:
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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