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

• Published in: Remote sensing (Basel, Switzerland) Vol. 16; no. 20; p. 3913
• Main Authors: Mantsis, Damianos F., Moumtzidou, Anastasia, Lioumbas, Ioannis, Gialampoukidis, Ilias, Christodoulou, Aikaterini, Mentes, Alexandros, Vrochidis, Stefanos, Kompatsiaris, Ioannis
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2024
• Subjects: Accuracy; Algorithms; Artificial neural networks; Atmospheric correction; Chlorophyll; Deep learning; Drinking water; Environmental monitoring; Formations; Hydrocarbons; Lakes; Machine learning; Neural networks; oil spill; Oil spills; Pollution detection; Polymers; Remote monitoring; Remote sensing; Satellite imagery; Satellites; Sensors; water body pollution; Water quality; Iran

The detection of complex formations, initially suspected to be oil spills, is investigated using atmospherically corrected multispectral satellite images and deep learning techniques. Several formations have been detected in an inland lake in Northern Greece. Four atmospheric corrections (ACOLITE, i...