Print Email Facebook Twitter Extracting Coastal Water Depths from Multi-Temporal Sentinel-2 Images Using Convolutional Neural Networks Title Extracting Coastal Water Depths from Multi-Temporal Sentinel-2 Images Using Convolutional Neural Networks Author Lumban-Gaol, Yustisi (Student TU Delft; National Research and Innovation Agency) Arroyo Ohori, G.A.K. (TU Delft Urban Data Science) Peters, R.Y. (TU Delft Urban Data Science) Date 2022 Abstract Satellite-Derived Bathymetry (SDB) can be calculated using analytical or empirical approaches. Analytical approaches require several water properties and assumptions, which might not be known. Empirical approaches rely on the linear relationship between reflectances and in-situ depths, but the relationship may not be entirely linear due to bottom type variation, water column effect, and noise. Machine learning approaches have been used to address nonlinearity, but those treat pixels independently, while adjacent pixels are spatially correlated in depth. Convolutional Neural Networks (CNN) can detect this characteristic of the local connectivity. Therefore, this paper conducts a study of SDB using CNN and compares the accuracies between different areas and different amounts of training data, i.e., single and multi-temporal images. Furthermore, this paper discusses the accuracies of SDB when a pre-trained CNN model from one or a combination of multiple locations is applied to a new location. The results show that the accuracy of SDB using the CNN method outperforms existing works with other methods. Multi-temporal images enhance the variety in the training data and improve the CNN accuracy. SDB computation using the pre-trained model shows several limitations at particular depths or when water conditions differ. Subject CNNinclude these here if the journal requires themmulti-temporal imagesSDBSentinel-2shallow watertransfer model To reference this document use: http://resolver.tudelft.nl/uuid:a20fa3bb-65d0-49a5-93b5-81eca0120c93 DOI https://doi.org/10.1080/01490419.2022.2091696 ISSN 0149-0419 Source Marine Geodesy: an international journal of ocean surveys, mapping and sensing, 45 (6), 615-644 Part of collection Institutional Repository Document type journal article Rights © 2022 Yustisi Lumban-Gaol, G.A.K. Arroyo Ohori, R.Y. Peters Files PDF Extracting_Coastal_Water_ ... tworks.pdf 6.15 MB Close viewer /islandora/object/uuid:a20fa3bb-65d0-49a5-93b5-81eca0120c93/datastream/OBJ/view