Synthesis of conceptual hierarchies applied to remote sensing images
Nicolas Louis, Jerzy J. Korczak · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1998
Remote sensing is a domain where one of the biggest important problems is the interpretation of large-sized images. Thereby, it is not possible for experts to analyze the ceaseless image streams. In practice, there is a growing interest in understanding concepts discovered in classified images. Our approach to image classifications is based on the conceptual clustering algorithm, Cobweb and its extensions. In general, these algorithms produce tree-structured clusters. However, once the hierarchies are built, the remote sensing experts need to compare and to synthesize the obtained hierarchies in terms of conceptual similarities. Two algorithms are described which produce a synthesis of hierarchies. The first algorithm can be used to synthesize results generated by heterogenous hierarchical classifiers, such as K-means, Unimem, Labyrinth. The second algorithm is an extended version of Cobweb. The experiments carried on urban zones have shown the universality and the efficiency of our approaches.