Optimal edge detection using multiple operators for image understanding

Extraction of features, such as edges for the understanding of aerial images, has been an important objective since the early days of remote sensing. This work aims at describing a new framework which allows for the quantitative combination of a preselected set of edge detectors based on the correspondence between their outcomes. This is inspired from the problem that despite the enormous amount of literature on edge detection techniques, there is no single technique that performs well in every possible image context. Two approaches are proposed for this purpose. The first approach is the well-known receiver operating characteristics analysis which is introduced for a sound quality evaluation of the edge maps estimated by combining different edge detectors. In the second approach, the so-called kappa statistics are employed in a novel fashion to amalgamate the above-mentioned selected edge maps to form an improved final edge image. This method is unique in the sense that the balance between the false detections (false positives and false negatives) is explicitly determined in advance and incorporated in the proposed method in a mathematical fashion. For the performance evaluation of the proposed techniques, a sample set of the RADIUS/DARPA-IU Fort Hood aerial image database with known ground truth has been used.