Detection of Complex Formations in an Inland Lake from Sentinel-2 Images Using Atmospheric Corrections and a Fully Connected Deep Neural Network

Damianos Mantsis; Anastasia Moumtzidou; Ioannis Lioumbas; Ilias Gialampoukidis; Aikaterini Christodoulou; Alexandros Mentes; Stefanos Vrochidis; Ioannis Kompatsiaris

doi:10.20944/preprints202410.0032.v1

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preprints.org > environmental and earth sciences > remote sensing > doi: 10.20944/preprints202410.0032.v1

Preprint Article Version 1 This version is not peer-reviewed

Detection of Complex Formations in an Inland Lake from Sentinel-2 Images Using Atmospheric Corrections and a Fully Connected Deep Neural Network

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Aikaterini Christodoulou

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Alexandros Mentes

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Stefanos Vrochidis

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Ioannis Kompatsiaris

Version 1 : Received: 16 September 2024 / Approved: 30 September 2024 / Online: 2 October 2024 (10:25:12 CEST)

How to cite: Mantsis, D.; Moumtzidou, A.; Lioumbas, I.; Gialampoukidis, I.; Christodoulou, A.; Mentes, A.; Vrochidis, S.; Kompatsiaris, I. Detection of Complex Formations in an Inland Lake from Sentinel-2 Images Using Atmospheric Corrections and a Fully Connected Deep Neural Network. Preprints 2024, 2024100032. https://doi.org/10.20944/preprints202410.0032.v1 Mantsis, D.; Moumtzidou, A.; Lioumbas, I.; Gialampoukidis, I.; Christodoulou, A.; Mentes, A.; Vrochidis, S.; Kompatsiaris, I. Detection of Complex Formations in an Inland Lake from Sentinel-2 Images Using Atmospheric Corrections and a Fully Connected Deep Neural Network. Preprints 2024, 2024100032. https://doi.org/10.20944/preprints202410.0032.v1

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MDPI and ACS Style

Mantsis, D.; Moumtzidou, A.; Lioumbas, I.; Gialampoukidis, I.; Christodoulou, A.; Mentes, A.; Vrochidis, S.; Kompatsiaris, I. Detection of Complex Formations in an Inland Lake from Sentinel-2 Images Using Atmospheric Corrections and a Fully Connected Deep Neural Network. Preprints 2024, 2024100032. https://doi.org/10.20944/preprints202410.0032.v1

APA Style

Mantsis, D., Moumtzidou, A., Lioumbas, I., Gialampoukidis, I., Christodoulou, A., Mentes, A., Vrochidis, S., & Kompatsiaris, I. (2024). Detection of Complex Formations in an Inland Lake from Sentinel-2 Images Using Atmospheric Corrections and a Fully Connected Deep Neural Network. Preprints. https://doi.org/10.20944/preprints202410.0032.v1

Chicago/Turabian Style

Mantsis, D., Stefanos Vrochidis and Ioannis Kompatsiaris. 2024 "Detection of Complex Formations in an Inland Lake from Sentinel-2 Images Using Atmospheric Corrections and a Fully Connected Deep Neural Network" Preprints. https://doi.org/10.20944/preprints202410.0032.v1

Abstract

The detection of complex formations, initially suspected to be oil spills, is investigated using atmospherically corrected multi-spectral satellite images and deep learning techniques. Several formations have been detected in an inland lake in Northern Greece. Four atmospheric corrections (ACOLITE, iCOR, Polymer, and C2RCC) that are specifically designed for water applications are examined and implemented on Sentinel-2 multispectral satellite images to eliminate the influence of the atmosphere. Out of the four algorithms, iCOR and ACOLITE are able to depict the formations sufficiently; however, the latter is chosen for further processing due to fewer uncertainties in the depiction of these formations as anomalies across the multi-spectral range. Furthermore, a number of formations are annotated at a pixel level for the 10 m bands (Red, Green, Blue, and NIR), and a deep neural network (DNN) is trained and validated. Our results show that the 4-band configuration provides the best model for the detection of these complex formations. Despite not being necessarily related to oil spills, studying these formations is crucial for environmental monitoring, pollution detection, and the advancement of remote sensing techniques.

Keywords

Sentinel-1 satellite; atmospheric corrections; oil spill; pollution over an inland lake

Subject

Environmental and Earth Sciences, Remote Sensing

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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