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2023
Contribution to book
Open Access
Mesoscale events classification in sea surface temperature imagery
Reggiannini M., Janeiro J., Martins F., Papini O., Pieri G.
Sea observation through remote sensing technologies plays an essential role in understanding the health status of marine fauna species and their future behaviour. Accurate knowledge of the marine habitat and the factors affecting faunal variations allows to perform predictions and adopt proper decisions. This is even more relevant nowadays, with policymakers needing increased environmental awareness, aiming to implement sustainable policies. There is a connection between the biogeochemical and physical processes taking place within a biological system and the variations observed in its faunal populations. Mesoscale phenomena, such as upwelling, countercurrents and filaments, are essential processes to analyse because their arousal entails, among other things, variations in the density of nutrient substances, in turn affecting the biological parameters of the habitat. This paper concerns the proposal of a classification system devoted to recognising marine mesoscale events. These phenomena are studied and monitored by analysing Sea Surface Temperature images captured by satellite missions, such as Metop and MODIS Terra/Aqua. Classification of such images is pursued through dedicated algorithms that extract temporal and spatial features from the data and apply a set of rules to the extracted features, in order to discriminate between different observed scenarios. The results presented in this work have been obtained by applying the proposed approach to images captured over the south-western region of the Iberian Peninsula.Source: Machine Learning, Optimization, and Data Science, edited by Nicosia G., Ojha V., La Malfa E., La Malfa G., Pardalos P., Di Fatta G., Giuffrida G., Umeton R., pp. 516–527, 2023
DOI: 10.1007/978-3-031-25599-1_38
Project(s): NAUTILOS
Metrics:
Reggiannini M., Janeiro J., Martins F., Papini O., Pieri G.
Sea observation through remote sensing technologies plays an essential role in understanding the health status of marine fauna species and their future behaviour. Accurate knowledge of the marine habitat and the factors affecting faunal variations allows to perform predictions and adopt proper decisions. This is even more relevant nowadays, with policymakers needing increased environmental awareness, aiming to implement sustainable policies. There is a connection between the biogeochemical and physical processes taking place within a biological system and the variations observed in its faunal populations. Mesoscale phenomena, such as upwelling, countercurrents and filaments, are essential processes to analyse because their arousal entails, among other things, variations in the density of nutrient substances, in turn affecting the biological parameters of the habitat. This paper concerns the proposal of a classification system devoted to recognising marine mesoscale events. These phenomena are studied and monitored by analysing Sea Surface Temperature images captured by satellite missions, such as Metop and MODIS Terra/Aqua. Classification of such images is pursued through dedicated algorithms that extract temporal and spatial features from the data and apply a set of rules to the extracted features, in order to discriminate between different observed scenarios. The results presented in this work have been obtained by applying the proposed approach to images captured over the south-western region of the Iberian Peninsula.Source: Machine Learning, Optimization, and Data Science, edited by Nicosia G., Ojha V., La Malfa E., La Malfa G., Pardalos P., Di Fatta G., Giuffrida G., Umeton R., pp. 516–527, 2023
DOI: 10.1007/978-3-031-25599-1_38
Project(s): NAUTILOS
Metrics:
See at:
ISTI Repository 
| link.springer.com 
| CNR ExploRA
2023
Conference article
Open Access
Automated image processing for remote sensing data classification
Reggiannini M., Papini O., Pieri G.
Remote sensing technologies allow for continuous and valuable monitoring of the Earth's various environments. In particular, coastal and ocean monitoring presents an intrinsic complexity that makes such monitoring the main source of information available. Oceans, being the largest but least observed habitat, have many different factors affecting theirs faunal variations. Enhancing the capabilities to monitor and understand the changes occurring allows us to perform predictions and adopt proper decisions. This paper proposes an automated classification tool to recognise specific marine mesoscale events. Typically, human experts monitor and analyse these events visually through remote sensing imagery, specifically addressing Sea Surface Temperature data. The extended availability of this kind of remote sensing data transforms this activity into a time-consuming and subjective interpretation of the information. For this reason, there is an increased need for automated or at least semi-automated tools to perform this task. The results presented in this work have been obtained by applying the proposed approach to images captured over the southwestern region of the Iberian Peninsula.Source: ICPR 2022 - International Workshops and Challenges, pp. 553–560, Montreal, Canada, 21-25/08/2022
DOI: 10.1007/978-3-031-37742-6_43
Project(s): NAUTILOS
Metrics:
Reggiannini M., Papini O., Pieri G.
Remote sensing technologies allow for continuous and valuable monitoring of the Earth's various environments. In particular, coastal and ocean monitoring presents an intrinsic complexity that makes such monitoring the main source of information available. Oceans, being the largest but least observed habitat, have many different factors affecting theirs faunal variations. Enhancing the capabilities to monitor and understand the changes occurring allows us to perform predictions and adopt proper decisions. This paper proposes an automated classification tool to recognise specific marine mesoscale events. Typically, human experts monitor and analyse these events visually through remote sensing imagery, specifically addressing Sea Surface Temperature data. The extended availability of this kind of remote sensing data transforms this activity into a time-consuming and subjective interpretation of the information. For this reason, there is an increased need for automated or at least semi-automated tools to perform this task. The results presented in this work have been obtained by applying the proposed approach to images captured over the southwestern region of the Iberian Peninsula.Source: ICPR 2022 - International Workshops and Challenges, pp. 553–560, Montreal, Canada, 21-25/08/2022
DOI: 10.1007/978-3-031-37742-6_43
Project(s): NAUTILOS
Metrics:
See at:
ISTI Repository | link.springer.com | CNR ExploRA
2023
Journal article
Open Access
MEC: a Mesoscale Events Classifier for oceanographic imagery
Pieri G., Janeiro J., Martins F., Papini O., Reggiannini M.
The observation of the sea through remote sensing technologies plays a fundamental role in understanding the state of health of marine fauna species and their behaviour. Mesoscale phenomena, such as upwelling, countercurrents, and filaments, are essential processes to be analysed because their occurrence involves, among other things, variations in the density of nutrients, which, in turn, influence the biological parameters of the habitat. Indeed, there is a connection between the biogeochemical and physical processes that occur within a biological system and the variations observed in its faunal populations. This paper concerns the proposal of an automatic classification system, namely the Mesoscale Events Classifier, dedicated to the recognition of marine mesoscale events. The proposed system is devoted to the study of these phenomena through the analysis of sea surface temperature images captured by satellite missions, such as EUMETSAT's Metop and NASA's Earth Observing System programmes. The classification of these images is obtained through (i) a preprocessing stage with the goal to provide a simultaneous representation of the spatial and temporal properties of the data and enhance the salient features of the sought phenomena, (ii) the extraction of temporal and spatial characteristics from the data and, finally, (iii) the application of a set of rules to discriminate between different observed scenarios. The results presented in this work were obtained by applying the proposed approach to images acquired in the southwestern region of the Iberian peninsula.Source: Applied sciences 13 (2023). doi:10.3390/app13031565
DOI: 10.3390/app13031565
Project(s): NAUTILOS
Metrics:
Pieri G., Janeiro J., Martins F., Papini O., Reggiannini M.
The observation of the sea through remote sensing technologies plays a fundamental role in understanding the state of health of marine fauna species and their behaviour. Mesoscale phenomena, such as upwelling, countercurrents, and filaments, are essential processes to be analysed because their occurrence involves, among other things, variations in the density of nutrients, which, in turn, influence the biological parameters of the habitat. Indeed, there is a connection between the biogeochemical and physical processes that occur within a biological system and the variations observed in its faunal populations. This paper concerns the proposal of an automatic classification system, namely the Mesoscale Events Classifier, dedicated to the recognition of marine mesoscale events. The proposed system is devoted to the study of these phenomena through the analysis of sea surface temperature images captured by satellite missions, such as EUMETSAT's Metop and NASA's Earth Observing System programmes. The classification of these images is obtained through (i) a preprocessing stage with the goal to provide a simultaneous representation of the spatial and temporal properties of the data and enhance the salient features of the sought phenomena, (ii) the extraction of temporal and spatial characteristics from the data and, finally, (iii) the application of a set of rules to discriminate between different observed scenarios. The results presented in this work were obtained by applying the proposed approach to images acquired in the southwestern region of the Iberian peninsula.Source: Applied sciences 13 (2023). doi:10.3390/app13031565
DOI: 10.3390/app13031565
Project(s): NAUTILOS
Metrics:
See at:
ISTI Repository | www.mdpi.com | CNR ExploRA
2023
Conference article
Open Access
Analysis of sea surface temperature maps via topological machine learning
Conti F., Papini O., Moroni D., Pieri G., Reggiannini M., Pascali M. A.
Computational methods to leverage topological features occurring in signals and images are currently one of the most innovative trends in applied mathematics. In this paper a pipeline of topological machine learning is applied to the challenging task of classifying four specific marine mesoscale patterns from remote sensing data, i.e., Sea Surface Temperature maps of the southwestern region of the Iberian Peninsula. Our preliminary study achieves an accuracy of 56% in the 4-label classification. Such results are encouraging, especially considering that the data are affected by noise and that there are low-quality/missing data. Also, the paper devises directions for future improvements.Source: ITNT 2023 - IX International Conference on Information Technology and Nanotechnology, Samara, Russia, 17-21/04/2023
DOI: 10.1109/itnt57377.2023.10139044
Project(s): NAUTILOS
Metrics:
Conti F., Papini O., Moroni D., Pieri G., Reggiannini M., Pascali M. A.
Computational methods to leverage topological features occurring in signals and images are currently one of the most innovative trends in applied mathematics. In this paper a pipeline of topological machine learning is applied to the challenging task of classifying four specific marine mesoscale patterns from remote sensing data, i.e., Sea Surface Temperature maps of the southwestern region of the Iberian Peninsula. Our preliminary study achieves an accuracy of 56% in the 4-label classification. Such results are encouraging, especially considering that the data are affected by noise and that there are low-quality/missing data. Also, the paper devises directions for future improvements.Source: ITNT 2023 - IX International Conference on Information Technology and Nanotechnology, Samara, Russia, 17-21/04/2023
DOI: 10.1109/itnt57377.2023.10139044
Project(s): NAUTILOS
Metrics:
See at:
ISTI Repository | ieeexplore.ieee.org | CNR ExploRA
2023
Conference article
Open Access
Evaluation of a marine mesoscale events classifier
Reggiannini M., Papini O., Pieri G.
Marine mesoscale phenomena are relevant oceanographic processes that impact on fishery, biodiversity and climate variation. In previous literature, their analysis has been tackled by processing instantaneous remote sensing observations and returning a classification of the observed event. Indeed, these phenomena occur within an extended time range, thus an analysis including time dependence is desirable. Mesoscale Events Classifier (MEC) is an algorithm devoted to the classification of marine mesoscale events in sea surface temperature imagery. By processing time series of satellite temperature observations MEC recognizes the considered area of interest as the domain of one out of a given number of possible events and returns the corresponding label. Objective of this work is to discuss the performance of the MEC pipeline in terms of its capability of correctly capturing the nature of the observed mesoscale process. The evaluation process exploited satellite remote sensing data collected in front of the Portuguese coast.Source: ICASSPW 2023 - IEEE International Conference on Acoustics, Speech, and Signal Processing, Rhodes, Greece, 4-10/06/2023
DOI: 10.1109/icasspw59220.2023.10193234
Project(s): NAUTILOS
Metrics:
Reggiannini M., Papini O., Pieri G.
Marine mesoscale phenomena are relevant oceanographic processes that impact on fishery, biodiversity and climate variation. In previous literature, their analysis has been tackled by processing instantaneous remote sensing observations and returning a classification of the observed event. Indeed, these phenomena occur within an extended time range, thus an analysis including time dependence is desirable. Mesoscale Events Classifier (MEC) is an algorithm devoted to the classification of marine mesoscale events in sea surface temperature imagery. By processing time series of satellite temperature observations MEC recognizes the considered area of interest as the domain of one out of a given number of possible events and returns the corresponding label. Objective of this work is to discuss the performance of the MEC pipeline in terms of its capability of correctly capturing the nature of the observed mesoscale process. The evaluation process exploited satellite remote sensing data collected in front of the Portuguese coast.Source: ICASSPW 2023 - IEEE International Conference on Acoustics, Speech, and Signal Processing, Rhodes, Greece, 4-10/06/2023
DOI: 10.1109/icasspw59220.2023.10193234
Project(s): NAUTILOS
Metrics:
See at:
ISTI Repository | ieeexplore.ieee.org | CNR ExploRA
2023
Software
Unknown
MEC: Mesoscale Events Classifier
Papini O.
This software consists of a Python 3 implementation of the Mesoscale Events Classifier (MEC) algorithm, which has been developed as part of the activities of Task 8.5 of the NAUTILOS project. The algorithm uses Sea Surface Temperature data coming from satellite missions to detect and classify patterns associated with "mesoscale events" in an upwelling ecosystem.Project(s): NAUTILOS
Papini O.
This software consists of a Python 3 implementation of the Mesoscale Events Classifier (MEC) algorithm, which has been developed as part of the activities of Task 8.5 of the NAUTILOS project. The algorithm uses Sea Surface Temperature data coming from satellite missions to detect and classify patterns associated with "mesoscale events" in an upwelling ecosystem.Project(s): NAUTILOS
See at: github.com | CNR ExploRA
2023
Report
Open Access
Mesoscale Events Classifier: an algorithm for the detection and classification of upwelling events using Sea Surface Temperature satellite data
Papini O.
In two previous technical reports we described a tool that produces a so-called spaghetti plot, i.e. a plot that is able to capture the trends of the sea surface temperature (SST) in a chosen time interval and within a target area; and the formalization of spaghetti plots through the definition of two custom Python 3 classes. In this report we outline an algorithm that uses SST data to detect and classify mesoscale upwelling events. In particular, the algorithm (called Mesoscale Events Classifier, MEC) takes as input the SST data organized as a SpaghettiData dictionary and returns a map of the area of interest where the zones in which the algorithm detects an event are highlighted and labelled with an event type.Source: ISTI Technical Report, ISTI-2023-TR/011, pp.1–20, 2023
DOI: 10.32079/isti-tr-2023/011
Project(s): NAUTILOS
Metrics:
Papini O.
In two previous technical reports we described a tool that produces a so-called spaghetti plot, i.e. a plot that is able to capture the trends of the sea surface temperature (SST) in a chosen time interval and within a target area; and the formalization of spaghetti plots through the definition of two custom Python 3 classes. In this report we outline an algorithm that uses SST data to detect and classify mesoscale upwelling events. In particular, the algorithm (called Mesoscale Events Classifier, MEC) takes as input the SST data organized as a SpaghettiData dictionary and returns a map of the area of interest where the zones in which the algorithm detects an event are highlighted and labelled with an event type.Source: ISTI Technical Report, ISTI-2023-TR/011, pp.1–20, 2023
DOI: 10.32079/isti-tr-2023/011
Project(s): NAUTILOS
Metrics:
See at:
ISTI Repository | CNR ExploRA
2022
Report
Open Access
SpaghettiData and SpaghettiPlot: two Python classes for analysing and visualising SST trends
Papini O.
This document describes the formalization of a "spaghetti plot" (i.e. a graph that captures the sea surface temperature trends in a target area) as a Python object, for which we defined two custom classes (SpaghettiData and SpaghettiPlot). In particular, we list the attributes and methods of these classes, together with the utilities that we use to create objects belonging to them.Source: ISTI Technical Report, ISTI-2022-TR/001, pp.1–8, 2022
DOI: 10.32079/isti-tr-2022/001
Project(s): NAUTILOS
Metrics:
Papini O.
This document describes the formalization of a "spaghetti plot" (i.e. a graph that captures the sea surface temperature trends in a target area) as a Python object, for which we defined two custom classes (SpaghettiData and SpaghettiPlot). In particular, we list the attributes and methods of these classes, together with the utilities that we use to create objects belonging to them.Source: ISTI Technical Report, ISTI-2022-TR/001, pp.1–8, 2022
DOI: 10.32079/isti-tr-2022/001
Project(s): NAUTILOS
Metrics:
See at:
ISTI Repository | CNR ExploRA
2022
Report
Open Access
NAUTILOS - Automatic image analysis tools
Pieri G., Reggiannini M., Papini O.
This deliverable will consist of the implementation of image analysis tools based on methods and algorithms designed explicitly to perform different automatic classifications. These tools will be used and applied both on already available and acquired images during the project. An accompanying report describing the tools will be produced.Source: ISTI Project report, NAUTILOS, DD8.9, 2022
Project(s): NAUTILOS
Pieri G., Reggiannini M., Papini O.
This deliverable will consist of the implementation of image analysis tools based on methods and algorithms designed explicitly to perform different automatic classifications. These tools will be used and applied both on already available and acquired images during the project. An accompanying report describing the tools will be produced.Source: ISTI Project report, NAUTILOS, DD8.9, 2022
Project(s): NAUTILOS
See at:
ISTI Repository | zenodo.org | CNR ExploRA
2022
Journal article
Open Access
An automated analysis tool for the classification of sea surface temperature imagery
Reggiannini M., Papini O., Pieri G.
Sea observation through remote sensing technologies plays an essential role in understanding the health status of the marine coastal environment, its fauna species and their future behavior. Accurate knowledge of the marine habitat and the factors affecting faunal variations allows us to perform predictions and adopt proper decisions. This paper concerns the proposal of a classification system devoted to recognizing marine mesoscale events. These phenomena are studied and monitored by analyzing sea surface temperature imagery. Currently, the standard way to perform such analysis relies on experts manually visualizing, analyzing, and tagging large imagery datasets. Nowadays, the availability of remote sensing data has increased so much that it is desirable to replace the labor-intensive, time-consuming, and subjective manual interpretation with automated analysis tools. The results presented in this work have been obtained by applying the proposed approach to images captured over the southwestern region of the Iberian Peninsula.Source: Pattern recognition and image analysis 32 (2022): 631–635. doi:10.1134/S1054661822030336
DOI: 10.1134/s1054661822030336
Project(s): NAUTILOS
Metrics:
Reggiannini M., Papini O., Pieri G.
Sea observation through remote sensing technologies plays an essential role in understanding the health status of the marine coastal environment, its fauna species and their future behavior. Accurate knowledge of the marine habitat and the factors affecting faunal variations allows us to perform predictions and adopt proper decisions. This paper concerns the proposal of a classification system devoted to recognizing marine mesoscale events. These phenomena are studied and monitored by analyzing sea surface temperature imagery. Currently, the standard way to perform such analysis relies on experts manually visualizing, analyzing, and tagging large imagery datasets. Nowadays, the availability of remote sensing data has increased so much that it is desirable to replace the labor-intensive, time-consuming, and subjective manual interpretation with automated analysis tools. The results presented in this work have been obtained by applying the proposed approach to images captured over the southwestern region of the Iberian Peninsula.Source: Pattern recognition and image analysis 32 (2022): 631–635. doi:10.1134/S1054661822030336
DOI: 10.1134/s1054661822030336
Project(s): NAUTILOS
Metrics:
See at:
ISTI Repository | link.springer.com | CNR ExploRA
2022
Report
Open Access
Studio e analisi delle architetture di reti convolutive
Moroni D., Papini O., Pascali M. A., Pieri G., Reggiannini M.
Questo rapporto tecnico di progetto è il risultato del contributo fornito dal Laboratorio Segnali e Immagini dell'ISTI-CNR per il documento di progetto RTOD-SYS-SDD-010-INT per il progetto RTOD (Real-Time Object Detection mediante Machine Learning basato su tecnologia Low-Power GPU). In particolare, il rapporto studia e discute delle varie possibilità di architetture di reti convolutive che sono state valutate e che potranno essere utilizzate nel contesto del progetto per effettuare delle categorizzazioni di immagini mediante algoritmi di machine learning.Source: ISTI Technical Report, ISTI-2022-TR/022, 2022
DOI: 10.32079/isti-tr-2022/022
Metrics:
Moroni D., Papini O., Pascali M. A., Pieri G., Reggiannini M.
Questo rapporto tecnico di progetto è il risultato del contributo fornito dal Laboratorio Segnali e Immagini dell'ISTI-CNR per il documento di progetto RTOD-SYS-SDD-010-INT per il progetto RTOD (Real-Time Object Detection mediante Machine Learning basato su tecnologia Low-Power GPU). In particolare, il rapporto studia e discute delle varie possibilità di architetture di reti convolutive che sono state valutate e che potranno essere utilizzate nel contesto del progetto per effettuare delle categorizzazioni di immagini mediante algoritmi di machine learning.Source: ISTI Technical Report, ISTI-2022-TR/022, 2022
DOI: 10.32079/isti-tr-2022/022
Metrics:
See at:
ISTI Repository | CNR ExploRA
2022
Report
Unknown
DEL-27: Nota tecnica sulle metodologie per la validazione di algoritmi di Machine Learning
D. Moroni, O. Papini, M. A. Pascali, G. Pieri, M. Reggiannini
La presente nota tecnica rappresenta l'output del work package 3102 dal titolo "Identificazione delle metodologie per la validazione di algoritmi di Machine Learning". L'obiettivo chiave è quello di identificare delle metodologie adatte alle reti neurali già studiate e proposte nel corso del progetto RTOD in modo da studiarne l'affidabilità e abilitarne il loro impiego anche in scenari operativi.Source: ISTI Project report, RTOD-TN-ML-027, 2022
D. Moroni, O. Papini, M. A. Pascali, G. Pieri, M. Reggiannini
La presente nota tecnica rappresenta l'output del work package 3102 dal titolo "Identificazione delle metodologie per la validazione di algoritmi di Machine Learning". L'obiettivo chiave è quello di identificare delle metodologie adatte alle reti neurali già studiate e proposte nel corso del progetto RTOD in modo da studiarne l'affidabilità e abilitarne il loro impiego anche in scenari operativi.Source: ISTI Project report, RTOD-TN-ML-027, 2022
See at: CNR ExploRA
2022
Report
Open Access
SI-Lab annual research report 2021
Righi M., Leone G. R., Carboni A., Caudai C., Colantonio S., Kuruoglu E. E., Leporini B., Magrini M., Paradisi P., Pascali M. A., Pieri G., Reggiannini M., Salerno E., Scozzari A., Tonazzini A., Fusco G., Galesi G., Martinelli M., Pardini F., Tampucci M., Berti A., Bruno A., Buongiorno R., Carloni G., Conti F., Germanese D., Ignesti G., Matarese F., Omrani A., Pachetti E., Papini O., Benassi A., Bertini G., Coltelli P., Tarabella L., Straface S., Salvetti O., Moroni D.
The Signal & Images Laboratory is an interdisciplinary research group in computer vision, signal analysis, intelligent vision systems and multimedia data understanding. It is part of the Institute of Information Science and Technologies (ISTI) of the National Research Council of Italy (CNR). This report accounts for the research activities of the Signal and Images Laboratory of the Institute of Information Science and Technologies during the year 2021.Source: ISTI Annual reports, 2022
DOI: 10.32079/isti-ar-2022/003
Metrics:
Righi M., Leone G. R., Carboni A., Caudai C., Colantonio S., Kuruoglu E. E., Leporini B., Magrini M., Paradisi P., Pascali M. A., Pieri G., Reggiannini M., Salerno E., Scozzari A., Tonazzini A., Fusco G., Galesi G., Martinelli M., Pardini F., Tampucci M., Berti A., Bruno A., Buongiorno R., Carloni G., Conti F., Germanese D., Ignesti G., Matarese F., Omrani A., Pachetti E., Papini O., Benassi A., Bertini G., Coltelli P., Tarabella L., Straface S., Salvetti O., Moroni D.
The Signal & Images Laboratory is an interdisciplinary research group in computer vision, signal analysis, intelligent vision systems and multimedia data understanding. It is part of the Institute of Information Science and Technologies (ISTI) of the National Research Council of Italy (CNR). This report accounts for the research activities of the Signal and Images Laboratory of the Institute of Information Science and Technologies during the year 2021.Source: ISTI Annual reports, 2022
DOI: 10.32079/isti-ar-2022/003
Metrics:
See at:
ISTI Repository | CNR ExploRA
2021
Report
Open Access
A tool for the temporal analysis of sea surface temperature maps
Papini O.
This document describes the usage of a tool that produces plots of the evolution of the sea surface temperature in a specified space-time window, extracting data from a series of NetCDF files.Source: ISTI-TR-2021/011, pp.1–8, 2021
DOI: 10.32079/isti-tr-2021/011
Project(s): NAUTILOS
Metrics:
Papini O.
This document describes the usage of a tool that produces plots of the evolution of the sea surface temperature in a specified space-time window, extracting data from a series of NetCDF files.Source: ISTI-TR-2021/011, pp.1–8, 2021
DOI: 10.32079/isti-tr-2021/011
Project(s): NAUTILOS
Metrics:
See at:
ISTI Repository | CNR ExploRA
2021
Conference article
Open Access
Mesoscale patterns identification through SST image processing
Reggiannini M., Janeiro J., Martins F., Papini O., Pieri G.
Mesoscale marine phenomena represent important features to understand and include within predictive models, which provide valuable information for proper environmental policy making. For example the rearrangement of the organic substances, consequent to the dynamics of the water masses affected by the mentioned phenomena, meaningfully modifies the actual condition of local habitats. Indeed it may facilitate the onset of non resident living species at the expense of resident ones, eventually affecting related human activity, such as commercial fishery. Objective of this work is the detection and identification of mesoscale events, in terms of specific marine surface patterns that are observed throughout such events, e.g. water filaments, countercurrents, meanders due to upwelling wind actions stress. These phenomena can be studied and monitored through the analysis of Sea Surface Temperature images captured by satellite missions, such as Metop, and MODIS Terra/Aqua. A quantitative description of such events is proposed, based on dedicated algorithms that extract temporal and spatial features from the images, and exploit them to provide a signature discriminating different observed scenarios. Preliminary results of the application of the proposed approach to a dataset related to the southwestern region of the Iberian Peninsula are presented.Source: ROBOVIS 2021 - 2nd International Conference on Robotics, Computer Vision and Intelligent Systems, pp. 165–172, Online Conference, 27-28/10/2021
DOI: 10.5220/0010714600003061
Project(s): NAUTILOS
Metrics:
Reggiannini M., Janeiro J., Martins F., Papini O., Pieri G.
Mesoscale marine phenomena represent important features to understand and include within predictive models, which provide valuable information for proper environmental policy making. For example the rearrangement of the organic substances, consequent to the dynamics of the water masses affected by the mentioned phenomena, meaningfully modifies the actual condition of local habitats. Indeed it may facilitate the onset of non resident living species at the expense of resident ones, eventually affecting related human activity, such as commercial fishery. Objective of this work is the detection and identification of mesoscale events, in terms of specific marine surface patterns that are observed throughout such events, e.g. water filaments, countercurrents, meanders due to upwelling wind actions stress. These phenomena can be studied and monitored through the analysis of Sea Surface Temperature images captured by satellite missions, such as Metop, and MODIS Terra/Aqua. A quantitative description of such events is proposed, based on dedicated algorithms that extract temporal and spatial features from the images, and exploit them to provide a signature discriminating different observed scenarios. Preliminary results of the application of the proposed approach to a dataset related to the southwestern region of the Iberian Peninsula are presented.Source: ROBOVIS 2021 - 2nd International Conference on Robotics, Computer Vision and Intelligent Systems, pp. 165–172, Online Conference, 27-28/10/2021
DOI: 10.5220/0010714600003061
Project(s): NAUTILOS
Metrics:
See at:
ISTI Repository | www.scitepress.org | CNR ExploRA
2021
Journal article
Open Access
SST image processing for mesoscale patterns identification
Papini O., Reggiannini M., Pieri G.
Understanding the marine environment dynamics to accordingly design computational predictive tools represents a factor of paramount relevance to implement suitable policy plans. In this framework mesoscale marine events are important to study and understand since human related activities, such as commercial fishery, strongly depend on this type of phenomena. Indeed the dynamics of water masses affect the local habitats due to nutrients and organic substances transport, interfering with the fauna and flora development processes. Mesoscale events can be classified based on the presence of specific hydrodynamics features, such as water filaments, counter-currents or meanders originating from upwelling wind actions stress. In this paper a novel method to study these phenomena is proposed, based on the analysis of Sea Surface Temperature imagery captured by satellite missions (Metop, MODIS Terra/Aqua). Dedicated algorithms are presented, with the goal to detect and identify different observed scenarios based on the extraction and analysis of discriminating quantitative features. Promising results returned by the application of the proposed method to data captured within the maritime region in front of the southwestern Iberian coasts are presented.Source: Engineering proceedings (Basel) 8 (2021). doi:10.3390/engproc2021008005
DOI: 10.3390/engproc2021008005
Project(s): NAUTILOS
Metrics:
Papini O., Reggiannini M., Pieri G.
Understanding the marine environment dynamics to accordingly design computational predictive tools represents a factor of paramount relevance to implement suitable policy plans. In this framework mesoscale marine events are important to study and understand since human related activities, such as commercial fishery, strongly depend on this type of phenomena. Indeed the dynamics of water masses affect the local habitats due to nutrients and organic substances transport, interfering with the fauna and flora development processes. Mesoscale events can be classified based on the presence of specific hydrodynamics features, such as water filaments, counter-currents or meanders originating from upwelling wind actions stress. In this paper a novel method to study these phenomena is proposed, based on the analysis of Sea Surface Temperature imagery captured by satellite missions (Metop, MODIS Terra/Aqua). Dedicated algorithms are presented, with the goal to detect and identify different observed scenarios based on the extraction and analysis of discriminating quantitative features. Promising results returned by the application of the proposed method to data captured within the maritime region in front of the southwestern Iberian coasts are presented.Source: Engineering proceedings (Basel) 8 (2021). doi:10.3390/engproc2021008005
DOI: 10.3390/engproc2021008005
Project(s): NAUTILOS
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ISTI Repository | ISTI Repository | www.mdpi.com | CNR ExploRA
2021
Contribution to conference
Open Access
Image processing applied to temperature pattern identification
Papini O., Pieri G., Reggiannini M.
The objective of our work is to detect and classify mesoscale patterns in an upwelling ecosystem by analysing Sea Surface Temperature (SST) maps coming from satellite data. The poster shows how we organize this information in a "spaghetti plot", a tool that we use to analyse different trends of the SST in a target area for a period of time, and how we can associate those trends with different mesoscale patterns.Source: ISTI Day(s) 2021, Online event, 23/11/2021
Project(s): NAUTILOS
Papini O., Pieri G., Reggiannini M.
The objective of our work is to detect and classify mesoscale patterns in an upwelling ecosystem by analysing Sea Surface Temperature (SST) maps coming from satellite data. The poster shows how we organize this information in a "spaghetti plot", a tool that we use to analyse different trends of the SST in a target area for a period of time, and how we can associate those trends with different mesoscale patterns.Source: ISTI Day(s) 2021, Online event, 23/11/2021
Project(s): NAUTILOS
See at:
ISTI Repository | CNR ExploRA