Hierarchical Segmentation Evaluation of Region-Based Image Hierarchy

Abstract

Multiscale image segmentation plays an important role in object-based image analysis (OBIA) applications, and the evaluation of segmentation quality is a hot topic for OBIA community. Recently, with the increasing use of multiscale and hierarchical strategy in OBIA works, region-based image hierarchies have attracted increasing attentions. A region-based image hierarchy (i.e., binary partition tree and scale-sets hierarchy) is a tree-like structure, and it can be used to record multiscale segmentation results. Although many methods have been employed to evaluate multiscale segmentation, they cannot be applied to evaluate a region-based image hierarchy directly. In this study, a hierarchical segmentation evaluation approach was proposed to evaluate the upper-bound accuracy of a region-based image hierarchy. In our study, an image hierarchy is implemented using a region merging process, and organized as a scale-indexed binary partition tree; the best matching segment of each reference polygon is then selected by scanning whole hierarchy; and finally, the similarity between reference polygons and corresponding best matching segments are used to evaluate the overall performance of the hierarchy. Two high spatial resolution images (GaoFen-2 and QuickBird) and three region merging criteria were used to evaluate the effectiveness of the proposed approach. Moreover, the proposed method was compared with traditional multiscale evaluation strategies. The experimental results have demonstrated the effectiveness of the proposed approach, as well as the advantages compared to existing approaches.