Effective Approach to Extract Road Map from Unmanned Aerial Vehicle Videos

Country disaster rescue is becoming more and more important and it requires a rapid response for disaster rescue. The key component for disaster rescue is to plan the optimal rescue path. Traditionally, the optimal rescue path seriously relies on the recognition on images of the damaged areas and the corresponding recognition algorithms are proposed for analyzing satellite images. However, due to its low updating frequency satellite images are not suitable for disaster rescue. Therefore, unmanned aerial vehicle is a good alternative approach to acquire real time images on damaged areas. Techniques are then needed to recognize the UAV images. To cope with this situation, we first extract UAV videos to images, segments these images into fixed size pieces, and manually labeled these data. We then study whether the conventional methods such as mathematical morphology, Hough transform and P-value segmentation approaches can be used to extract roads from UAV images. At last, we propose to adopt SVM and combine it with GA to improve the performance of this approach. Empirical studies are performed on data sets both collected by us and collected from the Internet. Experimental results demonstrate that our approach works well on these data sets when compared with conventional approaches. This indicates that UAV could be able to facilitate the country disaster rescue.