In the field of remote sensing image analysis, the recognition of complex patterns from satellite images presents several challenges related to the size, the accuracy and the complexity of the considered data. Indeed, due tothe large amount of ground details provided by these images, the classical photo-interpretation approachesdo not provide satisfactory results. In this context, it is then relevant to develop new automatic tools adaptedto the extraction of complex patterns from such data.In this thesis, we have proposed new region-based approaches (i.e., segmentation and classification) enablingto extract different levels of information from sets of images at different spatial resolutions. Indeed, suchmultiresolution sets of images provide different (complementary) views on the represented objects of interestand can be used to make easier the extraction process of these objects. The main principle of the propose d'approach is to progressively extract and classify segments/objects of interest from the lowest to the highestresolution data, and then finally to determine complex patterns from VHSR images. This approach, inspired by the principle of photo-interpretation and human vision, merges hierarchical segmentation approaches withmultiresolution clustering strategies combined to the integration of high-level background knowledge.The proposed framework has been validated in the context of the urban mapping of complex objects.Experiments have been carried out on multiresolution sets of satellite images sensed over different cities. Theresults obtained have shown that the quality and the accuracy of the extracted patterns seem sufficient tofurther accurately perform both classification or object detection in an operational context.
