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KNOWLEDGE-BASED INTERPRETATION OF REMOTE SENSING DATA WITH THE INTERIMAGE SYSTEM : MAJOR CHARACTERISTICS AND RECENT DEVELOPMENTS

The InterIMAGE Project aims at developing an open-source, knowledge-based framework for automatic interpretation of remote sensing data. The Project, which began in 2008, is leaded by the Computer Vision Lab of the Catholic University of Rio de Janeiro in collaboration with the Brazilian National Institute for Space Research (INPE) and the TNT Institute of the Hannover Leibniz University. The interpretation strategy implemented by InterIMAGE is based on a user defined knowledge model, structured through a semantic network. The nodes of the network represent concepts, classes of image objects expected to be found in a scene. Specialized image processing operators can be attached to the System, which controls their execution through a particular interpretation strategy, which is guided by the semantic network. The interpretation process carried out by InterIMAGE has two sequential, complementary phases. The first phase – the top-down step – is model-driven and generates hypotheses of the occurrence of image objects on the scene. The final instance network results from the data-driven, bottom-up analysis, responsible from judging the object hypotheses, validating or discarding them based on user defined decision rules. This paper focuses on describing the capabilities recently introduced in InterIMAGE, such as: multitemporal interpretation; debugging functionality; feature extraction and exploration; the graphical interface for creation of decision rules; and the System’s basic suite of image processing operators. In order to make a comprehensive description of these capabilities some simple examples will be also presented.