Abstract
Road is a kind of very typical artificial object. Road extraction from multi-scale remote sensing images is significant both in military field and in people's daily lives. With the development of remote sensing technology, the scale of remote sensing images that can be obtained becomes various. Therefore, the research of multi-scale remote sensing images is getting more and more attention and it is really a challenging task in the field of image processing. In this paper, a method of road extraction from multi-scale remote sensing images is proposed. Firstly, the textures are extracted and added to the bands of the original image. The filtering, resampling and segmentation operations are then implemented. Next, the spectral characteristics and textures of roads on the remote sensing images are statistically analyzed, and the changes of those on multi-scale remote sensing images are obtained. Then, considering the road characteristics displayed on remote sensing images, some parameters of spectral characteristics and textures are selected to extract roads using the object-oriented method. Finally, the results of road extraction are post-processed based on the opening and closing operation of mathematical morphology. This study has great significance in areas such as features optimization, target recognition, building feature database and improving the utilization of remote sensing data.
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