Integrating User Needs on Misclassification Error Sensitivity into Image Segmentation Quality Assessment

Abstract Commonly the assessment of the quality of image segmentations used in object-based land cover classification uses the geometric match between the derived segmentation and a reference dataset. This paper argues that a more appropriate assessment of a segmentation is to also consider the thematic content of the objects generated. This allows the assessment to be tailored to the needs of the specific user. A new method for image segmentation quality assessment is described, which combines a traditional geometric-only method with the thematic similarity index (TSI), a metric that expresses the degree of thematic quality of objects from a user’s perspective. The perspectives of two users (a wolf researcher and a general user of land cover information) were adopted in a case study to demonstrate the new method. The results show that the new method allowed the production of more accurate land cover classifications for the two users than the use of the geometric-only approach.