| A. Panagiotopoulou, E. Charou, K. Poirazidis, Y. Voutos, A. Martinis, L. Grammatikopoulos, E. Petsa, E. Bratsolis, Ph. Mylonas |
| Deep-Learning Based Super-Resolution of Sentinel-2 Images for Monitoring Supercentenarian Olive Trees |
| 25th Pan-Hellenic Conference on Informatics (PCI 2021), 26-28 November 2021, Volos, Greece |
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ABSTRACT
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| In the present work deep-learning based super-resolution (SR) is applied on Sentinel-2 images of the Zakynthos island, Greece, with the intention of detecting stress levels in supercentenarian olive trees due to water deficiency. The aim of this study is monitoring the stress in supercentenarian olive trees over time and over season. Specifically, the Carotenoid Reflectance Index 2 (CRI2) is calculated utilizing the Sentinel-2 bands B2 and B5. CRI2 maps at 10m and at 2.5mspatial resolutions are generated. In fact, the images of band B2 with original spatial resolution 10m are super-resolved to 2.5m. Regarding the images of band B5, these are SR resolved from 20m firstly to 10m and secondly to 2.5m. Deep-learning based SR techniques, namely DSen2 and RakSRGAN, are utilized for enhancing the spatial resolution to 10m and 2.5m. The following five seasons are considered autumn 2019, spring 2019, spring 2020, summer 2019 and summer 2020. In the future, comparisons with field measurements could better assess for the proposed methodology effectiveness regarding the recognition of stress levels in very old olive trees.
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| 26 November , 2021 |
| A. Panagiotopoulou, E. Charou, K. Poirazidis, Y. Voutos, A. Martinis, L. Grammatikopoulos, E. Petsa, E. Bratsolis, Ph. Mylonas, "Deep-Learning Based Super-Resolution of Sentinel-2 Images for Monitoring Supercentenarian Olive Trees", 25th Pan-Hellenic Conference on Informatics (PCI 2021), 26-28 November 2021, Volos, Greece |
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