Unsupervised Functional Information Clustering in Extreme Environments from Filter Banks and Relative Entropy

Regroupement non supervisé d'informations fonctionnelles dans des environnements extrêmes à partir de banques de filtres et d'entropie relative

Atto, Abdourrahmane M. ; Karbou, Fatima ; Giffard-Roisin, Sophie ; Bombrun, Lionel

Année de publication
2021

This chapter addresses feature extraction from wavelets and convnet convolutional neural network filters for unsupervised image time series analysis. It proposes the ability of wavelets and neuro-convolutional filters to capture non-trivial invariance properties, as well as the new centroid solutions, for high-level relative entropy-based feature analysis. The chapter considers unsupervised approaches without any requirement of the availability of training labels. Indeed, remote sensing imagery is a large source of images acquired in extreme geographical areas where collecting ground truths are usually very difficult. The chapter deals with a model selection framework summarizing the feature extraction. The texture analysis is performed over small image patches that are normalized to have zero-mean in the multivariate Gaussian case and unitary scale parameter in the Weibull-based modeling. The method proposed for functional image time series clustering relates to finding similarities between pixel evolution paths.</p>

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