remote sensing Article Rapid Single Image-Based DTM Estimation from ExoMars TGO CaSSIS Images Using Generative Adversarial U-Nets Yu Tao 1,* , Siting Xiong 2, Susan J. Conway 3, Jan-Peter Muller 1 , Anthony Guimpier 3, Peter Fawdon 4, Nicolas Thomas 5 and Gabriele Cremonese 6 1 Mullard Space Science Laboratory, Imaging Group, Department of Space and Climate Physics, University College London, Holmbury St Mary, Surrey RH5 6NT, UK;
[email protected] 2 College of Civil and Transportation Engineering, Shenzhen University, Shenzhen 518060, China;
[email protected] 3 Laboratoire de Planétologie et Géodynamique, CNRS, UMR 6112, Universités de Nantes, 44300 Nantes, France;
[email protected] (S.J.C.);
[email protected] (A.G.) 4 School of Physical Sciences, Open University, Walton Hall, Milton Keynes MK7 6AA, UK;
[email protected] 5 Physikalisches Institut, Universität Bern, Sidlerstrasse 5, 3012 Bern, Switzerland;
[email protected] 6 INAF, Osservatorio Astronomico di Padova, 35122 Padova, Italy;
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[email protected] Abstract: The lack of adequate stereo coverage and where available, lengthy processing time, various artefacts, and unsatisfactory quality and complexity of automating the selection of the best set of processing parameters, have long been big barriers for large-area planetary 3D mapping. In this paper, Citation: Tao, Y.; Xiong, S.; Conway, we propose a deep learning-based solution, called MADNet (Multi-scale generative Adversarial S.J.; Muller, J.-P.; Guimpier, A.; u-net with Dense convolutional and up-projection blocks), that avoids or resolves all of the above Fawdon, P.; Thomas, N.; Cremonese, G.