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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