A Novel Method for Fast Processing of Large Remote Sensed Image

Mancini, Adriano; Tassetti, Anna Nora; Cinnirella, Alessandro; Frontoni, Emanuele; Zingaretti, Primo

doi:10.1007/978-3-642-41184-7_42

Cited by 4 publications

(2 citation statements)

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“…Open-source Sentinel-1 data and the Search for Unidentified Maritime Objects (SUMO) algorithm [ 40 ] were used to detect ships and feed an algorithm that was developed to automatically fuse ship positions and fill data gaps due to non-reporting ships that could be hampered by technical limitations or deliberately switching off the system while concealing suspicious activities. SUMO, which works with a faster version of a pixel-based Constant False Alarm Rate (CFAR, [ 41 , 42 ]), was adopted as it represents a good compromise between performance and computational time. A case study in the central Adriatic Sea is analyzed and focus is placed in close proximity of managed areas, such as those surrounding the offshore gas platforms and the 3 nautical miles of the shoreline.…”

Section: Introductionmentioning

confidence: 99%

A Synergic Integration of AIS Data and SAR Imagery to Monitor Fisheries and Detect Suspicious Activities

Galdelli

Mancini

Ferrà

et al. 2021

Sensors

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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 “suspicious” AIS data gaps in close proximity of managed areas that can be further investigated only once the vessel—and the gear it adopts—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.

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Section: Introductionmentioning

confidence: 99%

A Synergic Integration of AIS Data and SAR Imagery to Monitor Fisheries and Detect Suspicious Activities

Galdelli

Mancini

Ferrà

et al. 2021

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…Many applications exploit the benefits of feature extraction and matching, such as registration of medical images [3], co-registration of very high resolution aerial / satellite images [4], [5], landing of an Unmanned Aerial Vehicle [6], [7], ship extraction [8], object recognition (e.g. face) [9], extraction of image correspondences or bundle adjustment [10], [11].…”

Section: Introductionmentioning

confidence: 99%