Using Generic Knowledge in Analysis of Aerial Scenes: A Case Study

Our goal is to produce high-quality symbolic descriptions from aerial scenes. We have chosen to work in the domain of large commercial airport complexes. Such scenes have a variety of features such as the transportation network, building structures, and mobile objects. This paper concentrates on detection and description of the transportation network (runways and taxiways). We illustrate the complexities of this problem and how it can be solved by using geometrical context and generic airport domain knowledge. 1 Introduction Automatic analysis of complex aerial images is an important and challenging problem. Our aim is to compute rich, symbolic descriptions from an image which may be used for a variety of tasks such as making a cartographic map, change detection and guidance. Aerial images are highly complex as they contain a large number (possibly hundreds) of both man-made and natural objects of a large variety, only some of which may be of interest. The image is also very complex, from a signal point of view, and sophisticated processing techniques are necessary. But, the scene can not be analyzed in terms of signal alone; we must also use our cultural and world knowledge to reduce the ambiguities inherent in images. The interesting issue, then, is what kind of knowledge is to be used and how. We have chosen major commercial airport complexes as a test domain. Airports contain a variety of objects, such as the transportation network (runways, taxiways, and roads), building structures (hangars, terminals, storage warehouses, fuel storage farms), and mobile objects (automobiles, aircraft, humans). The airport complexes are under continual changes, usually due to expansion. The images themselves are rather complex due to the large number of objects present in them. However, a variety of such images are available with only moderate effort. In our analysis, we do not assume specific knowledge of the scene, such as would be given by a detailed, current map of the specific airport complex. Instead, we only have generic information that the scene being viewed is an airport complex. We do have, and use, the knowledge about airports and the structures contained in them. Our approach in the design of the system is that it must be modular and that the modules interact mostly at high, symbolic levels. In airports, for example the modules may be for detecting and describing the transportation network, the buildings and the mobile objects. Detection of one type of …