Day 4

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Paper title AI4EO: from physics guided paradigms to quantum machine learning
Authors
  1. Mihai Datcu DLR - German Aerospace Center Speaker
Form of presentation Poster
Topics
  • C2. Digital Twins
    • C2.01 Towards a Digital Twin of the Earth - advances and challenges ahead
Abstract text AI4EO: from physics guided paradigms to quantum machine learning

Earth Observation (EO) Data Intelligence is addressing the entire value chain: data processing to extract information, the information analysis to gather knowledge, and knowledge transformation in value. EO technologies have immensely evolved the state of the art sensors deliver a broad variety of images, and have made considerable progress in spatial and radiometric resolution, target acquisition strategies, imaging modes, geographical coverage and data rates. Generally imaging sensors generate an isomorphic representation of the observed scene. This is not the case for EO, the observations are a doppelgänger of the scattered field, an indirect signature of the imaged object. EO images are instrument records, i.e. in addition to the spatial information, they are sensing physical parameters, and they are mainly sensing outside of the visual spectrum. This positions the load of EO image understanding, and the outmost challenge of Big EO Data Science, as new and particular challenge of Machine Learning (ML) and Artificial Intelligence (AI). The presentation introduces specific solutions for the EO Data Intelligence, as methods for physically meaningful features extraction to enable high accuracy characterization of any structure in large volumes of EO images. The theoretical background is introduced, discussing the advancement of the paradigms from Bayesian inference, machine learning, and evolving to the methods of Deep Learning and Quantum Machine Learning. The applications are demonstrated for: alleviation of atmospheric effects and retrieval of Sentinel 2 data, enhancing the opportunistic bi-static images with Sentinel 1, explainable data mining and discovery of physical scattering properties for SAR observations, and natural embedding of the PolSAR Stokes parameters in a gate-based quantum computer.

Coca Neagoe, M. Coca, C. Vaduva and M. Datcu, "Cross-Bands Information Transfer to Offset Ambiguities and Atmospheric Phenomena for Multispectral Data Visualization," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 14, pp. 11297-11310, 2021

U. Chaudhuri, S. Dey, M. Datcu, B. Banerjee and A. Bhattacharya, "Interband Retrieval and Classification Using the Multilabeled Sentinel-2 BigEarthNet Archive," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 14, pp. 9884-9898, 2021

A. Focsa, A. Anghel and M. Datcu, "A Compressive-Sensing Approach for Opportunistic Bistatic SAR Imaging Enhancement by Harnessing Sparse Multiaperture Data," in IEEE Transactions on Geoscience and Remote Sensing, early access

C. Karmakar, C. O. Dumitru, G. Schwarz and M. Datcu, "Feature-Free Explainable Data Mining in SAR Images Using Latent Dirichlet Allocation," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 14, pp. 676-689, 2021

Z. Huang, M. Datcu, Z. Pan, X. Qiu and B. Lei, "HDEC-TFA: An Unsupervised Learning Approach for Discovering Physical Scattering Properties of Single-Polarized SAR Image," in IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 4, pp. 3054-3071, April 2021

S. Otgonbaatar and M. Datcu, "Natural Embedding of the Stokes Parameters of Polarimetric Synthetic Aperture Radar Images in a Gate-Based Quantum Computer," in IEEE Transactions on Geoscience and Remote Sensing, early access