Mr. Ioannis Moutzouris-Sidiris Profile

Ioannis Moutzouris-Sidiris

at Univ of the Aegean

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Publications (3)

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Proceedings Article | 27 June 2019 Paper

An open source approach for oil spill detection using Sentinel-1 SAR images

Evangelia Efi Konstantinidou, Polychronis Kolokoussis, Konstantinos Topouzelis, Ioannis Moutzouris-Sidiris

Proceedings Volume 11174, 1117412 (2019) https://doi.org/10.1117/12.2539256

KEYWORDS: Synthetic aperture radar, Image segmentation, Databases, Radar, Image classification

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Marine domain awareness is important for crisis management in case of accidents but also as a surveillance tool against illicit discharge activities. Copernicus Sentinel-1 data are used for the detection of spills as they are provided free and in near real time. The availability of data and the recently developed cloud infrastructures give the opportunity for developing open source algorithms and techniques for oil spill detection. In this study a simple semi-automatic system for oil spill detection is presented for Sentinel-1 images. After image segmentation, the Orfeo Toolbox SVM vector classifier is trained based on the features extracted and separates the objects to possible oil spills and lookalikes. The method has been tested using three images depicting the evolution of an oil spill that resulted from a ship collision in the Mediterranean, offshore Corsica, on the 7th October 2018. Results were sound but further development is needed especially on algorithm training and feature extraction. All the software packages used are free and open source.

Proceedings Article | 27 June 2019 Paper

Assessment of chlorophyll-a concentration derived from Sentinel-3 satellite images using open source data

Ioannis Moutzouris-Sidiris, Konstantinos Topouzelis, Evangelia Efi Konstantinidou

Proceedings Volume 11174, 1117413 (2019) https://doi.org/10.1117/12.2535591

KEYWORDS: Satellites, Water, Satellite imaging, Earth observing sensors, In situ remote sensing, Ocean optics, Oceanography

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The concentration of chlorophyll-a is considered a very important water quality parameter due to the role it plays in the eutrophication. It can be estimated by remote sensing using empirical methods and/or semi-analytical methods. The main aim of this paper is to assess the chlorophyll-a concentration, derived from the Sentinel-3 Satellite, with in situ measurements covering the Mediterranean Sea. The Sentinel-3 Ocean and Land Colour Instrument (OLCI) was utilized. Two algorithms examined on their efficiency: i) the OC4Me Maximum Band Ratio algorithm, a polynomial algorithm based on the use of a semi-analytical model which uses a maximum band ratio approach of reflectances at 443, 490 and 510 nm, over the 560 nm and ii) a neural net (NN) algorithm, that uses an Inverse Radiative Transfer Model to estimate the water constitutes and estimate the chlorophyll-a concentration. A dedicated data set from the Copernicus Marine Environmental Service (CMES) with in situ chlorophyll-a concentrations was utilized. The parameters of interest were extracted and chlorophyll-a values at different depths were extracted. Also, to assure the accuracy of the in situ measurements the quality control parameters provided by the marine Copernicus were applied. The concentration of chlorophyll-a (C_M) at a penetration depth (Z_pd) was calculated from the in situ dataset. Then, a comparison was performed using the two algorithms against the in situ data. Statistical indexes were calculated to illustrate the correlation between the two algorithms and the in situ measurements. No significant correlation was observed for OC4Me algorithm. However, examining the time difference among the in situ data and the satellite acquisition the best correlation is observed between a time difference of two hours from the satellite and the in situ dataset. Last but not least, no correlation was observed between the chlorophyll-a calculated from the neural nets and the in situ dataset.

Proceedings Article | 6 August 2018 Paper

Assessment of chlorophyll-a retrievals algorithms from Sentinel-2 satellite data

Ioannis Moutzouris-Sidiris, Konstantinos Topouzelis

Proceedings Volume 10773, 1077303 (2018) https://doi.org/10.1117/12.2326675

KEYWORDS: Satellites, In situ metrology, Satellite imaging, Reflectivity, Ocean optics, Earth observing sensors, Water, Data modeling, Statistical analysis

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Remote sensing data can give the spatial and temporal distribution of chlorophyll-a, which is impossible with field measurements. Chlorophyll-a can be considered crucial due to the fact that it characterizes the level of eutrophication of a marine system. The major aim of this paper is to assess the chlorophyll-a retrieval algorithms from satellite images using in situ estimations in the region of Southern Aegean Sea. A data set from the Copernicus Marine Environmental Service (CMES) containing in situ chlorophyll-a concentrations was used to evaluate ocean color retrieval algorithms. Images captured from the Sentinel-2 satellite were used. Methodologically, the images were atmospherically corrected, pixel clouds were removed, and the Maximum Band Ratio was calculated. Then the ocean color algorithm for the Mediterranean Sea (MedΟC3) was used to calculate the chlorophyll-a concentrations. The in situ data measurements of chlorophyll-a concentrations were obtained at a depth of 20, 50, 75 and 100 m. The hypothesis for a homogenous sea was used (temperature difference of ΔΤ<0.2°C) in order to assume that the concentration of chlorophyll-a is the same at the surface as in 20 m depth. Α fourth order polynomial equation was fitted to the observed data for estimating the error of retrieval algorithm. Also, linear regression models were utilized between reflectance of a single band, logarithmically transformed band ratios of the visible spectrum and in situ concentrations of chlorophyll-a. Scatter plots, histograms and statistical indexes were calculated in order to evaluate the results. The best fit was calculated using the fourth order polynomial relationship between in situ and satellite data. On the contrary, linear regression model were not able to estimate accurately the chlorophyll-a concentration.

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