Ship Classification Methods for Sentinel-1 SAR Images

Duan, Jia; Wu, Yifeng; Luo, Jingsheng

doi:10.1007/978-981-13-9409-6_274

Cited by 2 publications

(2 citation statements)

References 12 publications

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“…Cooperative systems rely on self-reporting information from vessels providing details on identification, position and speed; this category includes data from Automatic Identification System (AIS), Long Range Identification and Tracking (LRIT) and the Vessel Monitoring System (VMS). On the other hand, non-cooperative systems employ radar and optical sensors (coastal, shipborne, airborne, and spaceborne) to detect ships from the background sea clutter without relying on their cooperation [ 12 , 13 , 14 ].…”

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

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“…The spatial resolutions available, normally of meters or tens of meters in SAR images relevant to maritime applications, thus constitute a limitation for the grain of feasible classification and the related performance. As far as deep learning is concerned, it is proving to be increasingly effective in marine object recognition [20][21][22], but the results produced from low-resolution data are not so overwhelmingly better than those obtained by algorithms based on handcrafted features [23][24][25]. In [26], it is proposed to improve the effectiveness of a convolutional neural network (CNN) classifier by leveraging handcrafted features in addition to the abstract features learned by the network.…”

Section: Ship Classificationmentioning

confidence: 99%

Remote Sensing for Maritime Traffic Understanding

Reggiannini,

Salerno,

Bacciu

et al. 2024

Remote Sensing

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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.

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Ship Classification Methods for Sentinel-1 SAR Images

Cited by 2 publications

References 12 publications

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

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

Remote Sensing for Maritime Traffic Understanding

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