A discrepancy measure for segmentation evaluation from the perspective of object recognition

Abstract

Within the framework of geographic object-based image analysis (GEOBIA), segmentation evaluation is one of the most important components and thus plays a critical role in controlling the quality of GEOBIA workflow. Among a variety of segmentation evaluation methods and criteria, discrepancy measurement is believed to be the most useful and is therefore one of the most commonly employed techniques in many applications. Existing measures have largely ignored the importance of object recognition in segmentation evaluation. In this study, a new discrepancy measure of segmentation evaluation index (SEI) redefines the corresponding segment using a two-sided 50% overlap instead of one-sided 50% overlap that has been commonly used. The effectiveness of SEI is further investigated using the schematic segmentation cases and remote sensing images. Results demonstrate that the proposed SEI outperforms the other two existing discrepancy measures, Euclidean Distance 2 (ED2) and Euclidean Distance 3 (ED3), both in terms of object recognition accuracy and identification of detailed segmentation differences.