@article {,
	title = {A Synergic Integration of AIS Data and SAR Imagery to Monitor Fisheries and Detect Suspicious Activities},
	journal = {Sensors},
	volume = {21},
	year = {2021},
	month = {04/2021},
	abstract = {<p>Maritime traffic and fishing activities have accelerated considerably over the last decade, with a consequent impact on the environment and marine resources. Meanwhile, a growing number of ship-reporting technologies and remote-sensing systems are generating an overwhelming amount of spatio-temporal and geographically distributed data related to large-scale vessels and their movements. Individual technologies have distinct limitations but, when combined, can provide a better view of what is happening at sea, lead to effectively monitor fishing activities, and help tackle the investigations of suspicious behaviors in close proximity of managed areas. The paper integrates non-cooperative Synthetic Aperture Radar (SAR) Sentinel-1 images and cooperative Automatic Identification System (AIS) data, by proposing two types of associations: (i) point-to-point and (ii) point-to-line. They allow the fusion of ship positions and highlight {\textquotedblleft}suspicious{\textquotedblright} AIS data gaps in close proximity of managed areas that can be further investigated only once the vessel{\textemdash}and the gear it adopts{\textemdash}is known. This is addressed by a machine-learning approach based on the Fast Fourier Transform that classifies single sea trips. The approach is tested on a case study in the central Adriatic Sea, automatically reporting AIS-SAR associations and seeking ships that are not broadcasting their positions (intentionally or not). Results allow the discrimination of collaborative and non-collaborative ships, playing a key role in detecting potential suspect behaviors especially in close proximity of managed areas.</p>
},
	keywords = {Automatic Identification System, Machine Learning, maritime surveillance, Synthetic Aperture Radar data integration},
	issn = {1424-8220},
	doi = {10.3390/s21082756},
	url = {https://www.mdpi.com/1424-8220/21/8/2756},
	author = {Alessandro Galdelli and Adriano Mancini and Carmen Ferra Vega and Anna Nora Tassetti}
}
@conference {,
	title = {A Synergic Photometric Stereo and Super Resolution Approach for Optical Inspection},
	booktitle = {2018 14th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA)},
	year = {2018},
	month = {2018},
	pages = {1-8},
	abstract = {<p>Three-dimensional reconstruction is a very important technique of Computer Vision that produces a three-dimensional (3D) model of a real scene. For this purpose, we use Photometric Stereo that allows you to shape estimation from several images under different lighting conditions. In the last few years, this technology has been used in many areas both working and scientific. An important sector where Photometric Stereo is used is quality control inside productive cycle. In some case, the model 3D is used to identify defects or to make measurements of the order of hundredths or even thousandths of millimeters. To obtain an accurate 3D model, it is necessary to use an cost-effective reconstruction system with a performance hardware. Alternatively, we propose a method that combine Photometric Stereo with Super Resolution that produces a very accurate model using cheap and lower performance hardware that is suitable for optical inspection. Indeed, the Super Resolution allows to overcome the hardware constraint and produce one high resolution image from a set of low resolution images. With this novel method, we obtain a very accurate 3D model that could be in a decision support system for optical inspection.</p>
},
	doi = {10.1109/MESA.2018.8449206},
	url = {https://doi.org/10.1109/MESA.2018.8449206},
	author = {Alessandro Galdelli and Adriano Mancini and Emanuele Frontoni and Primo Zingaretti}
}
