not yet published
Conference article
Open Access
Machine Learning Approaches for Automated Detection of Nephrops norvegicus Burrows in Underwater Surveys
Oscar Papini, Enrico Cecapolli, Filippo Domenichetti, Michela Martinelli, Gabriele Pieri, Marco Reggiannini, Lorenzo Zacchetti
This paper presents an analysis of computer vision methods designed to automate the detection, recognition, and classification of Nephrops norvegicus burrows in underwater videos. The proposed approach seeks to evaluate the accuracy, minimise human error, and standardise the existing manual video analysis process. By leveraging machine learning techniques, the system described in this paper autonomously processes video streams and identifies N. norvegicus burrow openings on the seabed. Additionally, this study investigates data augmentation algorithms to expand an annotated dataset and evaluates the performances of the first results under different configurations.Source: LECTURE NOTES IN COMPUTER SCIENCE. Kolkata, India, 01-05/12/2024
Project(s): NAUTILOS
Oscar Papini, Enrico Cecapolli, Filippo Domenichetti, Michela Martinelli, Gabriele Pieri, Marco Reggiannini, Lorenzo Zacchetti
This paper presents an analysis of computer vision methods designed to automate the detection, recognition, and classification of Nephrops norvegicus burrows in underwater videos. The proposed approach seeks to evaluate the accuracy, minimise human error, and standardise the existing manual video analysis process. By leveraging machine learning techniques, the system described in this paper autonomously processes video streams and identifies N. norvegicus burrow openings on the seabed. Additionally, this study investigates data augmentation algorithms to expand an annotated dataset and evaluates the performances of the first results under different configurations.Source: LECTURE NOTES IN COMPUTER SCIENCE. Kolkata, India, 01-05/12/2024
Project(s): NAUTILOS
2025
Contribution to book
Open Access
Computer vision to support Nephrops norvegicus imagery annotation
Marco Reggiannini, Enrico Cecapolli, Filippo Domenichetti, Michela Martinelli, Oscar Papini, Gabriele Pieri, Lorenzo Zacchetti
This document reports about the implementation of a computer vision procedure to estimate Nephrops norvegicus burrows density by analysing Underwater Television (UWTV) surveys. This activity, developed in cooperation with the ICES WGNEPS group, aims at providing an automatic system to support (i) the detection of the N. norvegicus openings, (ii) their grouping into systems (i.e. burrows) and (iii) the count of the distinct burrows. This could represent a relevant tool to simplify and optimise the stock assessment process.Source: ICES SCIENTIFIC REPORTS, pp. 23-28
DOI: 10.17895/ices.pub.28652012
Project(s): NAUTILOS
Metrics:
Marco Reggiannini, Enrico Cecapolli, Filippo Domenichetti, Michela Martinelli, Oscar Papini, Gabriele Pieri, Lorenzo Zacchetti
This document reports about the implementation of a computer vision procedure to estimate Nephrops norvegicus burrows density by analysing Underwater Television (UWTV) surveys. This activity, developed in cooperation with the ICES WGNEPS group, aims at providing an automatic system to support (i) the detection of the N. norvegicus openings, (ii) their grouping into systems (i.e. burrows) and (iii) the count of the distinct burrows. This could represent a relevant tool to simplify and optimise the stock assessment process.Source: ICES SCIENTIFIC REPORTS, pp. 23-28
DOI: 10.17895/ices.pub.28652012
Project(s): NAUTILOS
Metrics:
2024
Journal article
Open Access
Remote sensing for maritime traffic understanding
Reggiannini M, Salerno E, Bacciu C, D'Errico A, Lo Duca A, Marchetti A, Martinelli M, Mercurio C, Mistretta A, Righi M, Tampucci M, Di Paola C
The capability of prompt response in case of critical circumstances occurring within a maritime scenario depends on the awareness level of the competent authorities. From this perspective a quick and integrated surveillance service represents a tool of utmost importance. This is even more true when the main purpose is to tackle illegal activities such as smuggling, waste flooding or malicious vessel trafficking. This work presents an improved version of the OSIRIS system, a previously developed ICT framework devoted to understand the maritime vessel traffic through the exploitation of optical and radar data captured by satellite imaging sensors. A number of dedicated processing units are cascaded with the objective of i) detecting the presence of vessel targets in the input imagery, ii) estimating the vessel types on the basis of their geometric and scatterometric features, iii) estimating the vessel kinematics, iv) classifying the navigation behaviour of the vessel and predicting its route and, eventually, v) integrating the several outcomes within a webGIS interface to easily assess the traffic status inside the considered area. The entire processing pipeline has been tested on satellite imagery captured within the Mediterranean Sea or extracted from public, annotated data sets.Source: REMOTE SENSING (BASEL), vol. 16 (issue 3)
DOI: 10.3390/rs16030557
Metrics:
Reggiannini M, Salerno E, Bacciu C, D'Errico A, Lo Duca A, Marchetti A, Martinelli M, Mercurio C, Mistretta A, Righi M, Tampucci M, Di Paola C
The capability of prompt response in case of critical circumstances occurring within a maritime scenario depends on the awareness level of the competent authorities. From this perspective a quick and integrated surveillance service represents a tool of utmost importance. This is even more true when the main purpose is to tackle illegal activities such as smuggling, waste flooding or malicious vessel trafficking. This work presents an improved version of the OSIRIS system, a previously developed ICT framework devoted to understand the maritime vessel traffic through the exploitation of optical and radar data captured by satellite imaging sensors. A number of dedicated processing units are cascaded with the objective of i) detecting the presence of vessel targets in the input imagery, ii) estimating the vessel types on the basis of their geometric and scatterometric features, iii) estimating the vessel kinematics, iv) classifying the navigation behaviour of the vessel and predicting its route and, eventually, v) integrating the several outcomes within a webGIS interface to easily assess the traffic status inside the considered area. The entire processing pipeline has been tested on satellite imagery captured within the Mediterranean Sea or extracted from public, annotated data sets.Source: REMOTE SENSING (BASEL), vol. 16 (issue 3)
DOI: 10.3390/rs16030557
Metrics:
See at:
Remote Sensing 
| Remote Sensing | IRIS Cnr | IRIS Cnr | CNR IRIS 
| CNR IRIS
2024
Conference article
Open Access
Machine learning for the evaluation of the Nephrops norvegicus Population
Reggiannini M., Martinelli M., Papini O., Zacchetti L., Domenichetti F., Pieri G.
This paper introduces computer vision methods for detecting, recognising, and estimating Nephrops norvegicus (Norway lobster) burrow density via Underwater Television surveys. The current manual approach involves human operators visually assessing videos, which is prone to errors and subjectivity. Automated machine learning systems show promise in identifying and counting burrows, potentially standardising recognition and reducing operator errors. However, challenges exist in implementing computer vision techniques. An automated system aims to process video streams, detect seabed openings, extract visual features, and classify N. norvegicus burrows, significantly advancing the automation of underwater video reading. The primary processing presented in the paper lies in a boosting algorithm capable of extending the original annotated ground truth and assessing the improved performance of the extended data set with respect to the original one.Project(s): NAUTILOS
Reggiannini M., Martinelli M., Papini O., Zacchetti L., Domenichetti F., Pieri G.
This paper introduces computer vision methods for detecting, recognising, and estimating Nephrops norvegicus (Norway lobster) burrow density via Underwater Television surveys. The current manual approach involves human operators visually assessing videos, which is prone to errors and subjectivity. Automated machine learning systems show promise in identifying and counting burrows, potentially standardising recognition and reducing operator errors. However, challenges exist in implementing computer vision techniques. An automated system aims to process video streams, detect seabed openings, extract visual features, and classify N. norvegicus burrows, significantly advancing the automation of underwater video reading. The primary processing presented in the paper lies in a boosting algorithm capable of extending the original annotated ground truth and assessing the improved performance of the extended data set with respect to the original one.Project(s): NAUTILOS
2024
Conference article
Open Access
A mesoscale events classifier for sea surface temperature data
Reggiannini M., Papini O., Pieri G.
The identification of mesoscale phenomena, such as upwelling, countercurrents and filaments, is an important task for oceanographers. Indeed, the occurrence of such processes involves variations in the density of nutrients which, in turn, influences the biological parameters of the habitat. In this work, we describe a novel method for an automatic classification system, the Mesoscale Events Classifier (MEC), dedicated to recognising marine mesoscale events. MEC is devoted to the study of these phenomena through the analysis of Sea Surface Temperature (SST) images captured by satellite missions.Source: MISCELLANEA INGV, vol. 80, pp. 317-319. Bergen, Norvegia, 27-29/05/2024
DOI: 10.13127/misc/80/122
Project(s): NAUTILOS
Metrics:
Reggiannini M., Papini O., Pieri G.
The identification of mesoscale phenomena, such as upwelling, countercurrents and filaments, is an important task for oceanographers. Indeed, the occurrence of such processes involves variations in the density of nutrients which, in turn, influences the biological parameters of the habitat. In this work, we describe a novel method for an automatic classification system, the Mesoscale Events Classifier (MEC), dedicated to recognising marine mesoscale events. MEC is devoted to the study of these phenomena through the analysis of Sea Surface Temperature (SST) images captured by satellite missions.Source: MISCELLANEA INGV, vol. 80, pp. 317-319. Bergen, Norvegia, 27-29/05/2024
DOI: 10.13127/misc/80/122
Project(s): NAUTILOS
Metrics:
2024
Conference article
Open Access
Advancing sustainability: research initiatives at the Signals and Images Lab
Bruno A., Caudai C., Conti F., Leone G. R., Magrini M., Martinelli M., Moroni D., Muhammad A. Ch, Papini O., Pascali M. A., Pieri G., Reggiannini M., Righi M., Salerno E., Scozzari A., Tampucci M.
In this paper, we aim to briefly survey the relations of the work conducted at the Signals and Images Lab of CNR-ISTI, Pisa, with the themes of sustainability. We explore both the broader implications and the application-specific aspects of our work, highlighting references to published research and collaborative projects undertaken with key stakeholders and industrial partners.Source: CEUR WORKSHOP PROCEEDINGS, vol. 3762, pp. 499-504. Napoli, Italy, 29-30/05/2024
Bruno A., Caudai C., Conti F., Leone G. R., Magrini M., Martinelli M., Moroni D., Muhammad A. Ch, Papini O., Pascali M. A., Pieri G., Reggiannini M., Righi M., Salerno E., Scozzari A., Tampucci M.
In this paper, we aim to briefly survey the relations of the work conducted at the Signals and Images Lab of CNR-ISTI, Pisa, with the themes of sustainability. We explore both the broader implications and the application-specific aspects of our work, highlighting references to published research and collaborative projects undertaken with key stakeholders and industrial partners.Source: CEUR WORKSHOP PROCEEDINGS, vol. 3762, pp. 499-504. Napoli, Italy, 29-30/05/2024
See at:
ceur-ws.org | CNR IRIS | CNR IRIS
2024
Conference article
Restricted
Deep learning for SAR ship classification: focus on unbalanced datasets and inter-dataset generalization
Awais C. M., Reggiannini M.
Detection and recognition of vessel targets at sea are tasks of paramount relevance for maritime monitoring purposes. A possible approach to pursue these objectives consists in acquiring and processing Synthetic Aperture Radar (SAR) data related to a given area of interest. Classically, the detection part can be implemented by exploiting statistical properties of the signal to decide whether an image area belongs to background clutter or to a ship (e.g. Constant False Alarm Rate based algorithms). Successively, discriminant features referring to the detected object can be extracted and later fed to a classifier to decide the membership category of the considered target. Recently, thanks to the development of algorithms based on deep neural network architectures, object detection and recognition experienced an unprecedented boost in the observed performances. This work, mainly motivated by the exploration of these novel approaches to the identification of vessel targets, focuses on the analysis of five different deep learning architectures (CNN, pre-trained and non-pretrained versions of ResNet50 and VGG16) trained on two public SAR vessel datasets (OpenSARShip and Fusar). To address the data quantity limitation, a third dataset was created by merging both datasets.DOI: 10.1109/iceaa61917.2024.10701968
Project(s): National Biodiversity Future Center
Metrics:
Awais C. M., Reggiannini M.
Detection and recognition of vessel targets at sea are tasks of paramount relevance for maritime monitoring purposes. A possible approach to pursue these objectives consists in acquiring and processing Synthetic Aperture Radar (SAR) data related to a given area of interest. Classically, the detection part can be implemented by exploiting statistical properties of the signal to decide whether an image area belongs to background clutter or to a ship (e.g. Constant False Alarm Rate based algorithms). Successively, discriminant features referring to the detected object can be extracted and later fed to a classifier to decide the membership category of the considered target. Recently, thanks to the development of algorithms based on deep neural network architectures, object detection and recognition experienced an unprecedented boost in the observed performances. This work, mainly motivated by the exploration of these novel approaches to the identification of vessel targets, focuses on the analysis of five different deep learning architectures (CNN, pre-trained and non-pretrained versions of ResNet50 and VGG16) trained on two public SAR vessel datasets (OpenSARShip and Fusar). To address the data quantity limitation, a third dataset was created by merging both datasets.DOI: 10.1109/iceaa61917.2024.10701968
Project(s): National Biodiversity Future Center
Metrics:
2024
Other
Open Access
Imbalanced datasets through the lens of transfer-learning
Awais Ch Muhammad., Reggiannini M.
Data scarcity and class imbalance hinder deep learning for tasks like SAR ship classification. This work investigates how TRANSFER-LEARNING and DATA MERGING techniques can significantly improve the performance of deep learning models for class imbalanced datasets.Project(s): National Biodiversity Future Center
Awais Ch Muhammad., Reggiannini M.
Data scarcity and class imbalance hinder deep learning for tasks like SAR ship classification. This work investigates how TRANSFER-LEARNING and DATA MERGING techniques can significantly improve the performance of deep learning models for class imbalanced datasets.Project(s): National Biodiversity Future Center
See at:
CNR IRIS | www.nbfc.it | CNR IRIS
2024
Conference article
Open Access
Evaluating the Impact of fine-tuning on deep learning models for SAR ship classification
Awais Ch Muhammad, Reggiannini M.
Tasks like SAR ship classification suffer in deep learning due to data scarcity and class imbalance. To overcome these challenges, techniques like fine-tuning and data merging can play a vital role in the performance of a deep learning model. This study evaluates the effect of fine tuning on 5 different deep learning models.Project(s): National Biodiversity Future Center
Awais Ch Muhammad, Reggiannini M.
Tasks like SAR ship classification suffer in deep learning due to data scarcity and class imbalance. To overcome these challenges, techniques like fine-tuning and data merging can play a vital role in the performance of a deep learning model. This study evaluates the effect of fine tuning on 5 different deep learning models.Project(s): National Biodiversity Future Center
See at:
CNR IRIS | www.nbfc.it | CNR IRIS
2024
Other
Open Access
Future fishery and deep learning
Awais Ch Muhammad, Moroni D., Reggiannini M.
Ship classification algorithms based on the analysis of remote sensing data, e.g. Synthetic Aperture Radar (SAR) images, are examples of data processing methodologies that fulfill a crucial step in monitoring the marine environment. We compared the potential of different Deep Learning architectures with Vision Transformers (ViTs), a recent advancement in deep learning introducing an attention-based mechanism, for ship classification on imbalanced SAR data using F1-Score as metric.Project(s): National Biodiversity Future Center
Awais Ch Muhammad, Moroni D., Reggiannini M.
Ship classification algorithms based on the analysis of remote sensing data, e.g. Synthetic Aperture Radar (SAR) images, are examples of data processing methodologies that fulfill a crucial step in monitoring the marine environment. We compared the potential of different Deep Learning architectures with Vision Transformers (ViTs), a recent advancement in deep learning introducing an attention-based mechanism, for ship classification on imbalanced SAR data using F1-Score as metric.Project(s): National Biodiversity Future Center
2024
Journal article
Open Access
Advancing automated detection of Nephrops norvegicus burrows in underwater television surveys through machine learning
Papini O., Cecapolli E., Domenichetti F., Martinelli M., Pieri G., Reggiannini M., Zacchetti L.
The paper introduces computer vision methods for automating the detection, recognition, and classification of Nephrops norvegicus burrows in underwater videos. This approach aims to improve accuracy, reduce human errors, and standardize the current manual video analysis process. By using machine learning techniques, the system can automatically process video streams and detect N. norvegicus burrow openings on the seabed. The work also explores the use of data augmentation algorithms to extend the annotated data set, enhancing the performance of the automated system compared to the original manual annotations.Source: PATTERN RECOGNITION AND IMAGE ANALYSIS, vol. 34 (issue 4)
Project(s): NAUTILOS
Papini O., Cecapolli E., Domenichetti F., Martinelli M., Pieri G., Reggiannini M., Zacchetti L.
The paper introduces computer vision methods for automating the detection, recognition, and classification of Nephrops norvegicus burrows in underwater videos. This approach aims to improve accuracy, reduce human errors, and standardize the current manual video analysis process. By using machine learning techniques, the system can automatically process video streams and detect N. norvegicus burrow openings on the seabed. The work also explores the use of data augmentation algorithms to extend the annotated data set, enhancing the performance of the automated system compared to the original manual annotations.Source: PATTERN RECOGNITION AND IMAGE ANALYSIS, vol. 34 (issue 4)
Project(s): NAUTILOS
2024
Other
Open Access
A mesoscale events classifier for sea surface temperature data
Reggiannini M., Papini O., Pieri G.
The Mesoscale Events Classifier (MEC) is a tool that has been developed to detect and classify patterns of mesoscale events in an upwelling ecosystem by analysing Sea Surface Temperature (SST) maps coming from satellite data.Source: MISCELLANEA INGV, vol. 80. Bergen, Norvegia, 27-29/05/2024
Project(s): NAUTILOS
Reggiannini M., Papini O., Pieri G.
The Mesoscale Events Classifier (MEC) is a tool that has been developed to detect and classify patterns of mesoscale events in an upwelling ecosystem by analysing Sea Surface Temperature (SST) maps coming from satellite data.Source: MISCELLANEA INGV, vol. 80. Bergen, Norvegia, 27-29/05/2024
Project(s): NAUTILOS
See at:
CNR IRIS | share.ifremer.fr | CNR IRIS
2024
Conference article
Open Access
Testing a SAR-based ship classifier with different loss functions
Awais Ch. M., Reggiannini M., Moroni D.
This study investigated the influence of six different loss functions on Synthetic Aperture Radar (SAR) ship classification accuracy across two datasets. Kullback-Leibler Divergence Loss emerged with the highest average accuracy (69.5%), followed by L1 Loss (69.12%) and Focal Loss(68.4%). Interestingly, L1 and Focal Loss exhibited contrasting performance across datasets, suggesting potential data-specific suitability for certain functions. These findings highlight the importance of considering data characteristics and task requirements when selecting loss functions to optimize SAR ship classification performance.Project(s): National Biodiversity Future Center
Awais Ch. M., Reggiannini M., Moroni D.
This study investigated the influence of six different loss functions on Synthetic Aperture Radar (SAR) ship classification accuracy across two datasets. Kullback-Leibler Divergence Loss emerged with the highest average accuracy (69.5%), followed by L1 Loss (69.12%) and Focal Loss(68.4%). Interestingly, L1 and Focal Loss exhibited contrasting performance across datasets, suggesting potential data-specific suitability for certain functions. These findings highlight the importance of considering data characteristics and task requirements when selecting loss functions to optimize SAR ship classification performance.Project(s): National Biodiversity Future Center
See at:
eprints.bice.rm.cnr.it | CNR IRIS | CNR IRIS
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.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.DOI: 10.1007/978-3-031-25599-1_38
Project(s): NAUTILOS
Metrics:
See at:
CNR IRIS | link.springer.com | ISTI Repository | CNR IRIS | CNR IRIS
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.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.DOI: 10.1007/978-3-031-37742-6_43
Project(s): NAUTILOS
Metrics:
See at:
CNR IRIS | link.springer.com | ISTI Repository | CNR IRIS | CNR IRIS
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, vol. 13 (issue 3)
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, vol. 13 (issue 3)
DOI: 10.3390/app13031565
Project(s): NAUTILOS
Metrics:
See at:
CNR IRIS | ISTI Repository | www.mdpi.com | CNR IRIS
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.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.DOI: 10.1109/itnt57377.2023.10139044
Project(s): NAUTILOS
Metrics:
See at:
CNR IRIS | ieeexplore.ieee.org | ISTI Repository | CNR IRIS | CNR IRIS
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.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.DOI: 10.1109/icasspw59220.2023.10193234
Project(s): NAUTILOS
Metrics:
See at:
CNR IRIS | ieeexplore.ieee.org | ISTI Repository | CNR IRIS | CNR IRIS
2023
Conference article
Open Access
Evaluating the velocity of ships from low resolution SAR images
Reggiannini M, Salerno E
An abstract is not evaluableDOI: 10.1109/iceaa57318.2023.10297866
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Reggiannini M, Salerno E
An abstract is not evaluableDOI: 10.1109/iceaa57318.2023.10297866
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CNR IRIS | ieeexplore.ieee.org | ISTI Repository | CNR IRIS | CNR IRIS
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 Gr, Germanese D, Magrini M, Martinelli M, Moroni D, Pascali Ma, 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.DOI: 10.5281/zenodo.6322733
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Bruno A, Coscetti S, Leone Gr, Germanese D, Magrini M, Martinelli M, Moroni D, Pascali Ma, 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.DOI: 10.5281/zenodo.6322733
Metrics:
See at:
CNR IRIS | ISTI Repository | www.ital-ia2022.it | CNR IRIS