20.500.12556/DiRROS-19510 DINCAE 1.0 a convolutional neural network with error estimates to reconstruct sea surface temperature satellite observations A method to reconstruct missing data in sea surface temperature data using a neural network is presented. Satellite observations working in the optical and infrared bands are affected by clouds, which obscure part of the ocean underneath. In this paper, a neural network with the structure of a convolutional auto-encoder is developed to reconstruct the missing data based on the available cloud-free pixels in satellite images. Contrary to standard image reconstruction with neural networks, this application requires a method to handle missing data (or data with variable accuracy) in the training phase. The present work shows a consistent approach which uses the satellite data and its expected error variance as input and provides the reconstructed field along with its expected error variance as output. The neural network is trained by maximizing the likelihood of the observed value. The approach, called DINCAE (Data INterpolating Convolutional Auto-Encoder), is applied to a 25-year time series of Advanced Very High Resolution Radiometer (AVHRR) sea surface temperature data and compared to DINEOF (Data INterpolating Empirical Orthogonal Functions), a commonly used method to reconstruct missing data based on an EOF (empirical orthogonal function) decomposition. The reconstruction error of both approaches is computed using cross-validation and in situ observations from the World Ocean Database. DINCAE results have lower error while showing higher variability than the DINEOF reconstruction. strojno učenje konvolucijske nevronske mreže interpolacija površinska temperatura morja satelitske meritve true false true Angleški jezik Slovenski jezik Neznano 2024-07-19 03:42:04 2024-07-19 14:03:20 2024-07-20 04:03:07 0000-00-00 00:00:00 2020 0 0 str. 1609-1622 iss. 3 Vol. 13 2020 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 2020-03-27 004.85 1991-959X 10.5194/gmd-13-1609-2020 13432835 RAZ_Barth_Alexander_2020.pdf RAZ_Barth_Alexander_2020.pdf 1 0B393E588F89F99AD9051533F0B8D5B0 da55472f156a0d034ce77a34ab0c04cfdc8a61e650bb65762350831389c8cc52 5dde747d-45b6-11ef-b487-001a4af901a5 https://dirros.openscience.si/Dokument.php?lang=slv&id=27026 https://doi.org/10.5194/gmd-13-1609-2020 0 802fd2e7-45b6-11ef-b487-001a4af901a5 https://dirros.openscience.si/Dokument.php?lang=slv&id=27027 Nacionalni inštitut za biologijo 0 0 0