Segmentation is one of the key problems in remote sensing image processing, and a cluster hierarchy is a powerful tool
to analyze data on multiple scales. In this paper, we present an optimal hierarchical remote sensing image segmentation
approach which is a combination of feature-based classification, multi-scale region merging and classification-based
object refinement, and it is shown to have the following properties: 1) it gets better results on general remote sensing
images; 2) it offers a flexible segmentation hierarchy at a less computational cost.
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