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 
| CNR ExploRA | www.mdpi.com
2022
Conference article
Open Access
Augmented reality, artificial intelligence and machine learning in Industry 4.0: case studies at SI-Lab
Bruno A, Coscetti S, Leone G. R., Germanese D., Magrini M., Martinelli M., Moroni D., Pascali M. A., Pieri G., Reggiannini M., Tampucci M.
In recent years, the impressive advances in artificial intelligence, computer vision, pervasive computing, and augmented reality made them rise to pillars of the fourth industrial revolution. This short paper aims to provide a brief survey of current use cases in factory applications and industrial inspection under active development at the Signals and Images Lab, ISTI-CNR, Pisa.Source: Ital-IA 2022 - Convegno nazionale CINI sull'Intelligenza Artificiale, Torino, Italy, 9-11/02/2022
DOI: 10.5281/zenodo.6322733
Metrics:
Bruno A, Coscetti S, Leone G. R., Germanese D., Magrini M., Martinelli M., Moroni D., Pascali M. A., Pieri G., Reggiannini M., Tampucci M.
In recent years, the impressive advances in artificial intelligence, computer vision, pervasive computing, and augmented reality made them rise to pillars of the fourth industrial revolution. This short paper aims to provide a brief survey of current use cases in factory applications and industrial inspection under active development at the Signals and Images Lab, ISTI-CNR, Pisa.Source: Ital-IA 2022 - Convegno nazionale CINI sull'Intelligenza Artificiale, Torino, Italy, 9-11/02/2022
DOI: 10.5281/zenodo.6322733
Metrics:
See at:
ISTI Repository | CNR ExploRA | www.ital-ia2022.it
2022
Conference article
Restricted
A mobile crowdsensing app for improved maritime security and awareness
Moroni D., Pieri G., Reggiannini M., Tampucci M.
The marine and maritime domain is well represented in the Sustainable Development Goals (SDG) envisaged by the United Nations, which aim at conserving and using the oceans, seas and their resources for sustainable development. At the same time, there is a need for improved safety in navigation, especially in coastal areas. Up to date, there exist operational services based on advanced technologies, including remote sensing and in situ monitoring networks which provide aid to the navigation and control over the environment for its preservation. Yet, the possibilities offered by crowdsensing have not yet been fully explored. This paper addresses this issue by presenting an app based on a crowdsensing approach for improved safety and awareness at sea. The app can be integrated into more comprehensive systems and frameworks for environmental monitoring as envisaged in our future work.Source: PERCOM 2022 - IEEE International Conference on Pervasive Computing and Communications, pp. 103–105, Pisa, Italy, 21-25/03/2022
DOI: 10.1109/percomworkshops53856.2022.9767516
Project(s): NAUTILOS
Metrics:
Moroni D., Pieri G., Reggiannini M., Tampucci M.
The marine and maritime domain is well represented in the Sustainable Development Goals (SDG) envisaged by the United Nations, which aim at conserving and using the oceans, seas and their resources for sustainable development. At the same time, there is a need for improved safety in navigation, especially in coastal areas. Up to date, there exist operational services based on advanced technologies, including remote sensing and in situ monitoring networks which provide aid to the navigation and control over the environment for its preservation. Yet, the possibilities offered by crowdsensing have not yet been fully explored. This paper addresses this issue by presenting an app based on a crowdsensing approach for improved safety and awareness at sea. The app can be integrated into more comprehensive systems and frameworks for environmental monitoring as envisaged in our future work.Source: PERCOM 2022 - IEEE International Conference on Pervasive Computing and Communications, pp. 103–105, Pisa, Italy, 21-25/03/2022
DOI: 10.1109/percomworkshops53856.2022.9767516
Project(s): NAUTILOS
Metrics:
See at:
ieeexplore.ieee.org 
| 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 | CNR ExploRA | zenodo.org
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
Conference article
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: LOD 2022 - The 8th International Online & Onsite Conference on Machine Learning, Optimization, and Data Science, Certosa di Pontignano, Siena, Italy, 18-22/09/2022
Project(s): NAUTILOS
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: LOD 2022 - The 8th International Online & Onsite Conference on Machine Learning, Optimization, and Data Science, Certosa di Pontignano, Siena, Italy, 18-22/09/2022
Project(s): NAUTILOS
See at:
ISTI Repository | CNR ExploRA
2022
Contribution to book
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: IMTA VIII - 8th International Workshop Image Mining: Theory and Applications, edited by Davide Moroni, Heinrich Niemann, Maria Antonietta Pascali, Dietrich Paulus, Bernd Radig, Gerhard Ritter, Vera Yashina. Cham, Heidelberg, New York, Dordrecht, London: Springer, 2022
Project(s): NAUTILOS
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: IMTA VIII - 8th International Workshop Image Mining: Theory and Applications, edited by Davide Moroni, Heinrich Niemann, Maria Antonietta Pascali, Dietrich Paulus, Bernd Radig, Gerhard Ritter, Vera Yashina. Cham, Heidelberg, New York, Dordrecht, London: Springer, 2022
Project(s): NAUTILOS
See at:
ISTI Repository | 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
Restricted
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
Contribution to conference
Restricted
A mobile crowdsensing app for improved maritime security and awareness
Moroni D., Pieri G., Reggiannini M., Tampucci M.
The marine and maritime domain is well represented in the Sustainable Development Goals (SDG) envisaged by the United Nations, which aim at conserving and using the oceans, seas and their resources for sustainable development. At the same time, there is a need for improved safety in navigation, especially in coastal areas. Up to date, there exist operational services based on advanced technologies, including remote sensing and in situ monitoring networks which provide aid to the navigation and control over the environment for its preservation. Yet, the possibilities offered by crowdsensing have not yet been fully explored. This paper addresses this issue by presenting an app based on a crowdsensing approach for improved safety and awareness at sea. The app can be integrated into more comprehensive systems and frameworks for environmental monitoring as envisaged in our future work.Source: PERCOM 2022 - IEEE International Conference on Pervasive Computing and Communications, Pisa, Italy, 21-25/03/2022
DOI: 10.5281/zenodo.7713567
Project(s): NAUTILOS
Metrics:
Moroni D., Pieri G., Reggiannini M., Tampucci M.
The marine and maritime domain is well represented in the Sustainable Development Goals (SDG) envisaged by the United Nations, which aim at conserving and using the oceans, seas and their resources for sustainable development. At the same time, there is a need for improved safety in navigation, especially in coastal areas. Up to date, there exist operational services based on advanced technologies, including remote sensing and in situ monitoring networks which provide aid to the navigation and control over the environment for its preservation. Yet, the possibilities offered by crowdsensing have not yet been fully explored. This paper addresses this issue by presenting an app based on a crowdsensing approach for improved safety and awareness at sea. The app can be integrated into more comprehensive systems and frameworks for environmental monitoring as envisaged in our future work.Source: PERCOM 2022 - IEEE International Conference on Pervasive Computing and Communications, Pisa, Italy, 21-25/03/2022
DOI: 10.5281/zenodo.7713567
Project(s): NAUTILOS
Metrics:
See at:
CNR ExploRA
2021
Contribution to journal
Open Access
Special Issue "Remote sensing for maritime and water monitoring"
Pieri G., Reggiannini M.
Remote sensing plays a crucial role in the supervision of extended maritime areas. In particular, multisensor multisource imagery is collected daily through satellite platforms moving on earth-centered orbits. This massive amount of data, including optical images and radar maps, can be exploited for relevant purposes, such as the implementation of maritime surveillance tasks. These tasks typically address security and safety issues (vessel traffic monitoring, search and rescue) as well as environmental sustainability aspects (fishery, pollution). Satellite missions also serve the purpose of remotely measuring meaningful properties of the water column (e.g., salinity, turbidity, pollutants concentration), enabling chemophysical surveys of maritime areas of interest. Publications in this Special Issue will aim at composing a comprehensive overview of the several aspects that emerge in the implementation of maritime observation platforms through data processing. With these topics in mind, among various other subjects, the authors are invited to discuss theoretical issues and methods concerning remote sensing and data processing over the marine and maritime environment (such as software platforms for surveillance or monitoring, processing algorithms for radar or optical imagery, sea-level assessment through satellite altimetry, integrated methods for the observation of the maritime environment, etc.), not excluding application case studies focused on remote sensing within the maritime context. To this purpose, authors are invited to submit contributions that take into consideration the following topics: Maritime data representation, analysis, and learning, Ocean observation applied to fishing sustainability or biodiversity, Techniques for data processing applied to maritime observation, Target detection, classification, and identification in maritime data, Marine pollution monitoring along with sea environment monitoring issues, Open water analysis and trends based on remote sensing.Source: Basilea: Multidisciplinary Digital Publishing Institute (MDPI), 2021
Project(s): NAUTILOS
Pieri G., Reggiannini M.
Remote sensing plays a crucial role in the supervision of extended maritime areas. In particular, multisensor multisource imagery is collected daily through satellite platforms moving on earth-centered orbits. This massive amount of data, including optical images and radar maps, can be exploited for relevant purposes, such as the implementation of maritime surveillance tasks. These tasks typically address security and safety issues (vessel traffic monitoring, search and rescue) as well as environmental sustainability aspects (fishery, pollution). Satellite missions also serve the purpose of remotely measuring meaningful properties of the water column (e.g., salinity, turbidity, pollutants concentration), enabling chemophysical surveys of maritime areas of interest. Publications in this Special Issue will aim at composing a comprehensive overview of the several aspects that emerge in the implementation of maritime observation platforms through data processing. With these topics in mind, among various other subjects, the authors are invited to discuss theoretical issues and methods concerning remote sensing and data processing over the marine and maritime environment (such as software platforms for surveillance or monitoring, processing algorithms for radar or optical imagery, sea-level assessment through satellite altimetry, integrated methods for the observation of the maritime environment, etc.), not excluding application case studies focused on remote sensing within the maritime context. To this purpose, authors are invited to submit contributions that take into consideration the following topics: Maritime data representation, analysis, and learning, Ocean observation applied to fishing sustainability or biodiversity, Techniques for data processing applied to maritime observation, Target detection, classification, and identification in maritime data, Marine pollution monitoring along with sea environment monitoring issues, Open water analysis and trends based on remote sensing.Source: Basilea: Multidisciplinary Digital Publishing Institute (MDPI), 2021
Project(s): NAUTILOS
See at:
ISTI Repository | CNR ExploRA | www.mdpi.com
2021
Report
Open Access
SI-Lab Annual Research Report 2020
Leone G. R., Righi M., 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., Buongiorno R., Bruno A., Germanese D., Matarese F., Coscetti S., Coltelli P., Jalil B., Benassi A., Bertini G., Salvetti O., Moroni D.
The Signal & Images Laboratory (http://si.isti.cnr.it/) is an interdisciplinary research group in computer vision, signal analysis, smart vision systems and multimedia data understanding. It is part of the Institute for Information Science and Technologies of the National Research Council of Italy. This report accounts for the research activities of the Signal and Images Laboratory of the Institute of Information Science and Technologies during the year 2020.Source: ISTI Annual Report, ISTI-2021-AR/001, pp.1–38, 2021
DOI: 10.32079/isti-ar-2021/001
Metrics:
Leone G. R., Righi M., 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., Buongiorno R., Bruno A., Germanese D., Matarese F., Coscetti S., Coltelli P., Jalil B., Benassi A., Bertini G., Salvetti O., Moroni D.
The Signal & Images Laboratory (http://si.isti.cnr.it/) is an interdisciplinary research group in computer vision, signal analysis, smart vision systems and multimedia data understanding. It is part of the Institute for Information Science and Technologies of the National Research Council of Italy. This report accounts for the research activities of the Signal and Images Laboratory of the Institute of Information Science and Technologies during the year 2020.Source: ISTI Annual Report, ISTI-2021-AR/001, pp.1–38, 2021
DOI: 10.32079/isti-ar-2021/001
Metrics:
See at:
ISTI Repository | CNR ExploRA
2021
Report
Closed Access
OSIRIS-FO - OSIRIS PDR Meeting - CNR-ISTI current status
Salerno E., Martinelli M., Reggiannini M., Righi M., Tampucci M.
ESA OSIRIS 2 Project - Current status of CNR-ISTISource: ISTI Project report, OSIRIS-FO, 2021
Salerno E., Martinelli M., Reggiannini M., Righi M., Tampucci M.
ESA OSIRIS 2 Project - Current status of CNR-ISTISource: ISTI Project report, OSIRIS-FO, 2021
See at:
CNR ExploRA
2021
Report
Closed Access
Ship kinematics estimation based on doppler centroid deviation in synthetic aperture radar images
Reggiannini M.
This technical report describes a method, and the related software, to estimate the kinematics of a navigating vessel by processing Synthetic Aperture Radar data. The implemented software is included within the Ship Kinematics Estimation module in the framework of OSIRIS-FO (Optical/SAR data and System Integration for Rush Identification of Ship Models - Follow On).Source: ISTI Working papers, 2021
Reggiannini M.
This technical report describes a method, and the related software, to estimate the kinematics of a navigating vessel by processing Synthetic Aperture Radar data. The implemented software is included within the Ship Kinematics Estimation module in the framework of OSIRIS-FO (Optical/SAR data and System Integration for Rush Identification of Ship Models - Follow On).Source: ISTI Working papers, 2021
See at:
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 | CNR ExploRA | www.scitepress.org
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
Metrics:
See at:
CNR ExploRA | www.mdpi.com
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:
CNR ExploRA
2020
Contribution to conference
Open Access
Augmented reality and intelligent systems in Industry 4.0
Benassi A., Carboni A., Colantonio S., Coscetti S., Germanese D., Jalil B., Leone R., Magnavacca J., Magrini M., Martinelli M., Matarese F., Moroni D., Paradisi P., Pardini F., Pascali M., Pieri G., Reggiannini M., Righi M., Salvetti O., Tampucci M.
Augmented reality and intelligent systems in Industry 4.0 - Presentazione ARTESSource: ARTES, 12/11/2020
DOI: 10.5281/zenodo.4277713
DOI: 10.5281/zenodo.4277712
Metrics:
Benassi A., Carboni A., Colantonio S., Coscetti S., Germanese D., Jalil B., Leone R., Magnavacca J., Magrini M., Martinelli M., Matarese F., Moroni D., Paradisi P., Pardini F., Pascali M., Pieri G., Reggiannini M., Righi M., Salvetti O., Tampucci M.
Augmented reality and intelligent systems in Industry 4.0 - Presentazione ARTESSource: ARTES, 12/11/2020
DOI: 10.5281/zenodo.4277713
DOI: 10.5281/zenodo.4277712
Metrics:
See at:
ISTI Repository | CNR ExploRA
2020
Report
Closed Access
Optical/SAR data and system Integration for Rush Identification of Ship models OSIRIS 2 - ESA Project The Ground Truth Data Base
Martinelli M., Reggiannini M., Righi M., Salerno E., Tampucci M.
Presentazione Kick-Off MeetingSource: Project report, 2020
Martinelli M., Reggiannini M., Righi M., Salerno E., Tampucci M.
Presentazione Kick-Off MeetingSource: Project report, 2020
See at:
CNR ExploRA
2020
Report
Closed Access
OSIRIS-FO
Martinelli M., Moroni D., Reggiannini M., Righi M., Salerno E., Tampucci M.
OSIRIS-FO Ship Classification and Ship Kinematics EstimationSource: Project report, OSIRIS-FO, 2020
Martinelli M., Moroni D., Reggiannini M., Righi M., Salerno E., Tampucci M.
OSIRIS-FO Ship Classification and Ship Kinematics EstimationSource: Project report, OSIRIS-FO, 2020
See at:
CNR ExploRA