Talk by Mihai Datcu
(TerraData Workshop: ESIEE, Cité Descartes, 6 – 8 avenue Blaise Pascal, Marne la Vallée, June 25th, 2018, 14:00)
The sensory gap is the dissimilarity between the actual nature of an object and the information extracted from the signals recorded by a sensor observing this object. Earth Observation (EO) images are sensor records, gathering the signature of the observed scenes in a specific electromagnetic spectrum, therefore an indirect signature of the imaged scenes. The challenge is in inverting the physically meaningful parameters of the scene from these observations. The lecture is presenting a communication channel model for the parameter retrieval problem. The source is the ensemble of EO data, and the information contained in the data is a message. Modeling the data processing chain as a communication channel allows measuring and quantifying the amount of information each feature descriptor can provide about a set of images. The generative Bayesian models, as Latent Dirichlet Allocation (LDA), Restricted Boltzmann Machine, Generative Adversarial Networks and the latest Deep Learning paradigms are discussed and exemplified as solutions for the EO sensory gap.