Interactive object recognition by keypoint models

In the field of remote sensing, image-based object recognition can benefit from direct measurement of characteristic dimensions. But in many cases image resolution and collateral data about the mapping properties do not provide sufficient precision for the intended purposes of object identification. In such cases, the recognition performance can often be increased significantly by coupling several characteristic dimensions and taking their mutual relations into account. The approach proposed in this paper defines so-called `keypoint models' that describe certain object classes by the geometrical arrangement of characteristic features, denoted as keypoints. A feature space is spanned by the normalized distances of these keypoints. The complexity of the models as expressed by the number of keypoints is scalable and gets selected according to the specific recognition needs. Different model variants cover the significance of object features according to the spectral sensitivity of the sensor. Keypoints are intended to be marked interactively by an image analyst while taking into account the inaccuracies caused by image resolution and other possible ambiguities. We demonstrate our approach in the example domain of airplanes where the complexity of our actually most sophisticated model amounts to ten keypoints. We place special emphasis on the aspects of intuitive usability for image analysts working under time pressure. Perspectives of integrating automatic feature-extraction techniques are also discussed briefly.