Automatic learning of structural knowledge from geographic information for updating land cover maps

Abstract : The number of satellites and remote sensing sensors devoted to earth observation becomes increasingly high, providing more and more data and especially images. In the same time the access to such a data and to the tools to process them has been considerably improved. In the presence of such data flow - and regarding the necessity to follow up and predict environmental and societal changes in highly dynamic socio-environmental contexts - we need automatic image interpretation methods. This could be accomplished by exploring some strengths of artificial intelligence. Our main idea consists in inducing classification rules that explicitly take into account structural knowledge, using Aleph, an Inductive Logic Programming (ILP) system. We applied our proposed methodology to three land cover/use maps of the French Guiana littoral. One hundred and forty six classification rules were induced for the 39 land-cover classes of the maps. These rules are expressed in first order logic language which make them intelligible and interpretable by non-experts. A ten-fold cross validation gave average values for classification accuracy, specificity and sensibility equal to, respectively, 98.82 %, 99.65% and 70%. The proposed methodology could be valuably exploited to automatically classify new objects and/or help operators using object-based classification procedures.
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Meriam Bayoudh, Emmanuel Roux, Richard Nock, G. Richard. Automatic learning of structural knowledge from geographic information for updating land cover maps. Symposium of the Latin American Society for Remote Sensing and Spatial Information Systems (SELPER), Nov 2012, Cayenne, French Guiana. 10 p. multigr. ⟨ird-00797337⟩

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