Hierarchical Bayesian image analysis: From low-level modeling to robust supervised learning. (January 2019) - Lagrange, Adrien; Fauvel, Mathieu; May, Stéphane; Dobigeon, Nicolas
Highlights: This paper proposes a unified framework to perform classification and low-level modeling jointly. Robustness is improved by considering a possibly badly labeled training set. The proposed model allows a very rich interpretation of the modeled data structure. Performance is assessed on synthetic and real data in the specific context of hyperspectral image interpretation. The proposed model is generic enough to incorporate any kind of low-level modeling. Abstract: Within a supervised classification framework, labeled data are used to learn classifier parameters. Prior to that, it is generally required to perform dimensionality reduction via feature extraction. These preprocessing steps have motivated numerous research works aiming at recovering latent variables in an unsupervised context. This paper proposes a unified framework to perform classification and low-level modeling jointly. The main objective is to use the estimated latent variables as features for classification and to incorporate simultaneously supervised information to help latent variable extraction. The proposed hierarchical Bayesian model is divided into three stages: a first low-level modeling stage to estimate latent variables, a second stage clustering these features into statistically homogeneous groups and a last classification stage exploiting the (possibly badly) labeled data. Performance of the model is assessed in the specific context of hyperspectral image interpretation, unifying two standard analysis techniques, namely unmixing and classification.