Apprentissage statistique de classes sémantiques pour l'interprétation d'images aériennes

Abstract : This work is about interpretation of the content of very high resolution aerial optical panchromatic images. Two methods are proposed for the classification of this kind of images. The first method aims at detecting the instances of a class of objects and the other method aims at segmenting superpixels extracted from the images using a contextual model of the relations between the superpixels. The object detection method in very high resolution images uses a mixture of appearance models of a class of objects then fuses the hypothesis returned by the models. We develop a method that clusters training samples into visual subcategories based on a two stages procedure using metadata and visual information. The clustering part allows to learn models that are specialised in recognizing a subset of the dataset and whose fusion lead to a generalization of the object detector. The performances of the method are evaluate on several dataset of very high resolution images at several resolutions and several places. The method proposed for contextual semantic segmentation use a combination of visual description of a superpixel extract from the image and contextual information gathered between a superpixel and its neighbors. The contextual representation is based on a graph where the nodes are the superpixels and the edges are the relations between two neighbors. Finally we predict the category of a superpixel using the predictions made by of the neighbors using the contextual model in order to make the prediction more reliable. We test our method on a dataset of very high resolution images.
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https://tel.archives-ouvertes.fr/tel-01482119
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Submitted on : Friday, January 12, 2018 - 6:38:07 PM
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Hicham Randrianarivo. Apprentissage statistique de classes sémantiques pour l'interprétation d'images aériennes. Traitement du signal et de l'image [eess.SP]. Conservatoire national des arts et metiers - CNAM, 2016. Français. ⟨NNT : 2016CNAM1117⟩. ⟨tel-01482119v2⟩

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