Remote Sensing for Maritime Prompt Monitoring

Reggiannini, Marco; Righi, Marco; Tampucci, Marco; Duca, Angelica Lo; Bacciu, Clara; Bedini, Luigi; D’Errico, Andrea; Paola, Claudio Di; Marchetti, Andrea; Martinelli, Massimo; Mercurio, Costanzo; Salerno, Emanuele; Zizi, Bruno

doi:10.3390/jmse7070202

Cited by 12 publications

(13 citation statements)

References 39 publications

(45 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This method still requires traversing the entire data in the pre-screening step. Other studies [18] describe a software platform dedicated to sea surveillance, capable of detecting and identifying illegal maritime traffic. This platform results from the cascade pipeline of several image processing algorithms that input radar or optical imagery captured by satellite-borne sensors and try to identify vessel targets in the scene and provide quantitative descriptors about their shape and motion.…”

Section: The Analyses Of the Traditional Methodsmentioning

confidence: 99%

An Adaptive Hierarchical Detection Method for Ship Targets in High-Resolution SAR Images

et al. 2020

View full text Add to dashboard Cite

With the improvement of image resolution in synthetic aperture radars (SARs), sea clutter characteristics become more complex, which poses new challenges to traditional ship target detection missions. In this paper, to detect ship targets quickly and efficiently in a complex background, we propose an adaptive hierarchical detection method based on a coarse-to-fine mechanism. This method constructs a new visual attention mechanism to strengthen ship targets and obtain the candidate targets adaptively by the means dichotomy method. On this basis, the precise detection results of the targets are obtained using the speed block kernel density estimation method, which maintains constant false alarm characteristics. Compared with existing methods, the adaptive hierarchical detection method has simple, fast, and accurate characteristics. Experiments based on GF-III satellite and airborne SAR datasets are presented to demonstrate the effectiveness of the proposed method.

show abstract

Section: The Analyses Of the Traditional Methodsmentioning

confidence: 99%

An Adaptive Hierarchical Detection Method for Ship Targets in High-Resolution SAR Images

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Radar and SAR have been used for sea state monitoring, oil spill or ship detection, exploiting the capability of mapping large-scale areas, as well as the possibility to be operated with any weather and light (diurnal and nocturnal) conditions. In [4], we present the module used in our platform to implement the task of ship detection based on SAR imagery, which feeds the related downstream modules. The detection technique used is based on a Constant False Alarm Rate (CFAR) approach.…”

Section: Ship Detectionmentioning

confidence: 99%

“…In this paper, we first summarize the desired features of a marine traffic understanding system, with reference to the available literature; then, we present a real implementation developed within the framework of the European Space Agency (ESA) project OSIRIS Follow-On, which is the upgraded version of OSIRIS (Optical and SAR data and system integration for rush identification of ship models [4]), a former ESA project ended in 2018.…”

Section: Introductionmentioning

confidence: 99%

Remote Sensing for Maritime Traffic Understanding

Reggiannini,

Salerno,

Bacciu

et al. 2024

Remote Sensing

Self Cite

View full text Add to dashboard Cite

The capability of prompt response in the 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 Information and Communication Technology framework devoted to understanding 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 behavior 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 datasets.

show abstract

“…The paper focusses on SRP and extends a previous work described in Lo Duca et al (2017), which described a SRP algorithm based on the K-nearest neighbor (K-NN) classifier (Dasarathy, 1991). The described work is part of the Optical/SAR data and system integration for rush identification of ship models (OSIRIS) project [7] (Reggiannini et al , 2019), whose aim involves building a support decision system for maritime surveillance.…”

Section: Introductionmentioning

confidence: 99%

Exploiting multiclass classification algorithms for the prediction of ship routes: a study in the area of Malta

Duca

Marchetti

2020

JSIT

Self Cite

View full text Add to dashboard Cite

Purpose Ship route prediction (SRP) is a quite complicated task, which enables the determination of the next position of a ship after a given period of time, given its current position. This paper aims to describe a study, which compares five families of multiclass classification algorithms to perform SRP. Design/methodology/approach Tested algorithm families include: Naive Bayes (NB), nearest neighbors, decision trees, linear algorithms and extension from binary. A common structure for all the algorithm families was implemented and adapted to the specific case, according to the test to be done. The tests were done on one month of real data extracted from automatic identification system messages, collected around the island of Malta. Findings Experiments show that K-nearest neighbors and decision trees algorithms outperform all the other algorithms. Experiments also demonstrate that linear algorithms and NB have a very poor performance. Research limitations/implications This study is limited to the area surrounding Malta. Thus, findings cannot be generalized to every context. However, the methodology presented is general and can help other researchers in this area to choose appropriate methods for their problems. Practical implications The results of this study can be exploited by applications for maritime surveillance to build decision support systems to monitor and predict ship routes in a given area. For example, to protect the marine environment, the use of SRP techniques could be used to protect areas at risk such as marine protected areas, from illegal fishing. Originality/value The paper proposes a solid methodology to perform tests on SRP, based on a series of important machine learning algorithms for the prediction.

show abstract

Remote Sensing for Maritime Prompt Monitoring

Cited by 12 publications

References 39 publications

An Adaptive Hierarchical Detection Method for Ship Targets in High-Resolution SAR Images

An Adaptive Hierarchical Detection Method for Ship Targets in High-Resolution SAR Images

Remote Sensing for Maritime Traffic Understanding

Exploiting multiclass classification algorithms for the prediction of ship routes: a study in the area of Malta

Contact Info

Product

Resources

About