Knowledge Based Interpretation of Moorland in Aerial Images

For the interpretation of remote sensing data the traditional methods such as multispectral classification are in many cases not sufficient. This applies especially to more complex scenes. In order to interpret such scenes it is necessary to include and use more prior knowledge about the depicted objects, e.g. knowledge about the possible object structure or, in a multitemporal interpretation, knowledge about the possible temporal changes. In this paper we present an approach for the automatic interpretation of moorland from aerial images. The first step is a monotemporal interpretation. We use a knowledge based system with an explicit knowledge representation through semantic nets. This system is suitable to formulate explicitly (i.e. in a standard language) prior knowledge and to use it for the interpretation. In our case we divided moorland into different relevant land use classes and described them in a semantic net. For every class we described the obligatory parts. Obligatory parts are features and structures, which have to be detected in the particular areas in order to assign them the corresponding class. Because in moorland areas monitoring of changes is very important we extended the monotemporal system to a multitemporal one. The multitemporal interpretation also exploits explicitly represented prior knowledge about the possible temporal changes. The results show that the presented approach is suitable for the interpretation of moorland. The exploited additional prior knowledge led to an improvement of the interpretation, especially for the multitemporal one.