A Scale-Synthesis Method for High Spatial Resolution Remote Sensing Image Segmentation

Multiscale segmentation is always needed to extract semantic meaningful objects for object-based remote sensing image analysis. Choosing the appropriate segmentation scales for distinct ground objects and intelligently combining them together are two crucial issues to get the appropriate segmentation result for target applications. With respect to these two issues, this paper proposes a simple scale-synthesis method which is highly flexible to be adjusted to meet the segmentation requirements of varying image-analysis tasks. The main idea of this method is to first divide the whole image area into multiple regions; each region consisted of ground objects that have similar optimal segmentation scale. Then, synthesize the suboptimal segmentations of each region to get the final segmentation result. The result is the combination of suboptimal scales of objects and is therefore more coherent to ground objects. To validate this method, the land-cover-category map is used to guide the scale synthesis of multiscale image segmentations for the Quickbird-image land-use classification. First, the image is coarsely divided into multiple regions; each region belongs to a certain land-cover category. Then, multiscale-segmentation results are generated by the Mumford-Shah function based region-merging method. For each land-cover category, the optimal segmentation scale is selected by the supervised segmentation-accuracy-assessment method. Finally, the optimal scales of segmentation results are synthesized under the guide of land-cover category. It is proved that the proposed scale-synthesis method can generate a more accurate segmentation result that benefits the latter classification. The land-use-classification accuracy reaches to 77.8%.