Abstract
A common strategy for road extraction from remote sensing images is classification based on spectral information. However, due to a common phenomenon that different objects can be with similar spectral characteristics, classification results usually contain many interference regions which do not correspond to any road entity. To solve this problem, a road extraction method based on direction consistency segmentation is proposed in this paper. In binary road classification images, considering that road regions in these images usually have consistent local directions, pixels with similar main directions are merged into objects. After acquiring these objects, geometric measurements such as LFI (Linear Feature Index) and region area are calculated and a segment-linking algorithm is used to recognize and extract road objects among them. Various test images are used to verify the effectiveness of this method and contrast experiments are performed between the proposed binary image processing method and two existing methods. Experimental results show that this method has advantages in both accuracy, computational efficiency and stability, which can be used to extract road regions in remote sensing images at different resolutions.
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Ding, L., Yang, Q., Lu, J., Xu, J., Yu, J. (2016). Road Extraction Based on Direction Consistency Segmentation. In: Tan, T., Li, X., Chen, X., Zhou, J., Yang, J., Cheng, H. (eds) Pattern Recognition. CCPR 2016. Communications in Computer and Information Science, vol 662. Springer, Singapore. https://doi.org/10.1007/978-981-10-3002-4_11
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DOI: https://doi.org/10.1007/978-981-10-3002-4_11
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