Abstract
Abstract. Road network detection from very high resolution satellite and aerial images is highly important for diverse domains. Although an expert can label road pixels in a given image, this operation is prone to error and quite time consuming remembering that road maps must be updated regularly. Therefore, various computer vision based automated algorithms have been proposed in the last two decades. Nevertheless, due to the diversity of scenes, the field is still open for robust methods which might detect roads on different resolution images of different type of environments. In this study, we picked an earlier proposed road detection method which works based on traditional computer vision and probability theory algorithms. We improved it by further steps using reinforcement learning theory. With the help of the novel hybrid technique (traditional computer vision method combined with reinforcement learning based artificial intelligence), we achieved a solution that we call RLSnake. This new method can learn new image scenes and resolutions rapidly and can work reliably. We believe that the proposed RLSnake will be a significant step in the remote sensing field in order to develop solutions which might increase performance by combining the power of traditional and new techniques.
Highlights
Road network detection from a satellite or aerial image is an important and challenging remote sensing problem
I.e. the RLSNAKE, we benefit from a reinforcement learning (RL) based artificial intelligence framework for completing the road segments which were not detected by the first module
This indicates that the traditional computer vision and probability based method is not suitable to be used with different resolution images, unless an intensive research is performed for adjusting the all parameter set of the algorithm for the new sensor images
Summary
Road network detection from a satellite or aerial image is an important and challenging remote sensing problem. Potential solutions might help with automatic update of the road maps. The resolutions of the recent satellite and aerial imaging sensors allow developing algorithms which might extract roads segments. The traditional computer vision techniques are not able to offer robust solutions for automatic segmentation due to high variance of the scene. Road segments might have different intensity values and different widths. Junctions of unknown number of roads and roundabouts may increase the difficulty of the problem. Roads can be occluded by other nearby objects like buildings, trees and high number of vehicles on the road. There is still need for advanced methods to extract road networks from high resolution satellite or aerial images
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