In remote sensing imagery, ground objects belonging to the same land cover category always have similar optimal
segmentation scales. The paper proposed a method using the land cover categories as a prior knowledge to guide the
synthesis of multi-scale image segmentation results. This method took into account the variety of scale characteristics of
different ground objects as well as the similarity of scale of objects belonging to the same land cover category. Firstly,
the image was coarsely divided into multiple regions, and each of them belonged to a land cover category. Then for each
category, we selected the optimal segmentation scale by the supervised accuracy assessment of segmentation results.
Finally, the optimal scales of segmentation results were synthesized to get the final segmentation result. To validate this
method, the Quickbird image was segmented and classified. Experimental results showed that this method could
generate accurate segmentation results for the latter classification.