An Accuracy Assessment Measure for Object Based Image Segmentation

Traditional approaches to accuracy assessment are inadequate for object oriented image processing. We tested some measures to assess the accuracy of object based image segmentation in a supervised context. The measures quantify the extent to which objects in the segmentation match training objects in terms of over-segmentation, under-segmentation, and distance to a perfect match. Using high resolution digital aerial photographs over an urban setup, we obtained segmentation results for a variety of parameter combinations using two software packages: eCognition and ASTRO. We compute the accuracy measures using three types of objects: vehicles, trees and buildings. The measures were used to compare the software, identify ideal parameter combinations, and identify objects that each software is better at extracting from the images. The measures are shown to be an intuitive, useful technique for consistency checking different segmentation results and assessing segmentation accuracies among a large set of disparate segmentation results.