Learning fuzzy rules to characterize objects of interest from remote sensing images

B. Belarte (1) , C. Wemmert (1) , G. Forestier (2) , M. Grizonnet (3) , C. Weber (4)

1 ICube - Laboratoire des sciences de l'ingénieur, de l'informatique et de l'imagerie
2 MIPS - Modélisation, Intelligence, Processus et Système
3 CNES - Centre National d'Études Spatiales [Toulouse]
4 LIVE - Laboratoire Image, Ville, Environnement

In this article a new method for learning concepts from examples of objects provided by experts for remote sensing images is presented. The goal of this method is to give the geographer expert a description of complex objects of interest extracted from very high resolution remote sensing images. The description of such objects needs to handle imprecision inherent to segmentation and very high resolution images. The first step of this approach is to classify objects composing all the examples. This classification allows the learning of a rule describing how the examples are composed regarding the segmentation. Finally, this rule is used to extract objects corresponding to the examples. Experiments on a remote sensing image of a urban landscape in Toulouse, France are presented to show the relevance of the method.

Domaines: Environnement et Société; Traitement du signal et de l'image [eess.SP]

B. Belarte, C. Wemmert, G. Forestier, M. Grizonnet, C. Weber. Learning fuzzy rules to characterize objects of interest from remote sensing images. IEEE Geoscience and Remote Sensing Symposium (IGARSS), Jul 2013, Melbourne, France. ⟨10.1109/IGARSS.2013.6723453⟩. ⟨hal-01888500⟩