Méthodes d'analyse de texture pour la cartographie d'occupations du sol par télédetection très haute résolution : application à la fôret, la vigne et les parcs ostréicoles

Abstract : The prime objective of this thesis is to evaluate the potential of multivariate probabilistic models applied on wavelet subbands for the classification of very high resolution remote sensing optical data. Three main applications are investigated in this study: the differentiation of age classes of maritime pine forest stands, the detection of vineyards and the detection of oyster fields. One main contribution includes the proposal of an original supervised and object-oriented classification scheme based on similarity measurements adapted to the context of probabilistic modeling. This scheme involves the creation of a database of texture patches for the learning step and a pre-segmentation of the image to classify. The tested multivariate models were first evaluated in an image retrieval framework. The best models identified in this procedure were then applied in the proposed image processing scheme. In the three proposed thematic applications, multivariate models revealed remarkable abilities to represent the texture and reached higher classification accuracies than the method based on co-occurrence matrices. These results confirm the interest of the multi-scale and multi-orientation representation of textures through the wavelet transform, as well as the relevance of the multivariate modeling of wavelet coefficients
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Olivier Regniers. Méthodes d'analyse de texture pour la cartographie d'occupations du sol par télédetection très haute résolution : application à la fôret, la vigne et les parcs ostréicoles. Traitement du signal et de l'image [eess.SP]. Université de Bordeaux, 2014. Français. ⟨NNT : 2014BORD0284⟩. ⟨tel-01152599⟩

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