Parametric approaches for the segmentation and the classification of textures
This approach is motivated by the assumption that segmentation may be more reliable when performed on a sharper image and that the quality of the deconvolution result may be improved by the knowledge of the regions composing the image. Concerning classification and model selection, we are also working on methods for indirect observation (e.g. including convolution). Proposed solutions are based on the computation of evidences (marginal likelihoods). In both cases, the major difficulty comes from the joint deconvolution. This is addressed within a hierarchical Bayesian framework, estimation/classification tasks being performed by stochastic sampling agorithms.
Current work also concerns the modeling of either time, spectrometric or polarimetric dependences. The multi-scale representation of 2-D or 3-D spatial processes relies on various families of probabilistic models (copulas, generalized multivariate Gaussian densities, SIRV models, etc.). Texture anisotropy is described using the local structure tensor field, itself handled by means of Riemanian probabilistic models of SPD matrices. Many of these theoretical approaches and algorithmic tools have been applied to very high resolution (VHR) remote sensed images for agronomic and environmental purposes : identification and characterization of forest stands, vineyards and oyster racks, etc.
These activities have benefited from the financial support of various entities including the Conseil Regional d’Aquitaine, the french Agence Nationale de la Recherche, the CPU and COTE clusters within Bordeaux University, the Centre National d’Etudes Spatiales (CNES) and the group Safran.