| Abstract |
Automated visual analysis has substantially advanced in recent years, allowing a variety of targets to be automatically detected. Remarkably successful algorithms and technologies have been developed, e.g., for face detection and for obstacle detection for autonomous car navigation. In archaeology, however, remote sensing images are still analyzed in the traditional way, by a visual inspection. Such a visual inspection is performed prior to a field survey in order to identify potential sites that may guide later fieldwork. Though this approach saves fieldwork time, visual inspection remains very time consuming and requires the highly concentrated attention of an expert. Due to human fatigue, this approach might be unreliable. Moreover, the visual inspection of image data over vast unexplored areas is not feasible at all. This is especially frustrating, since a large amount of high resolution image data has become available due to recent developments of satellite technology. It is, therefore, very appealing to automate screening of large datasets of remote sensing images. |