The best of a BAD situation: Optimising an algorithm to match course resolution SAR vessel detections to sparse AIS data

Meyer, R. G. V.; Schwegmann, C. P.; Kleynhans, W.

doi:10.1109/igarss.2017.8127471

Cited by 3 publications

(2 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These can be grouped by the source of information they use. Those that use images to detect the type of ship, including Synthetic Aperture Radar (SAR) [ 26 , 27 ] or photographs [ 28 ], use a different approach based on analysis of the pixels of static images. Comparative (terrain reference) navigation represents an approach similar to the use of images to explore the environment, although it does not require a satellite; in [ 29 ] the use of 3D multibeam sonar data is explored.…”

Section: Related Workmentioning

confidence: 99%

Architecture for Trajectory-Based Fishing Ship Classification with AIS Data

Pedroche

Amigo

García

et al. 2020

Sensors

View full text Add to dashboard Cite

This paper proposes a data preparation process for managing real-world kinematic data and detecting fishing vessels. The solution is a binary classification that classifies ship trajectories into either fishing or non-fishing ships. The data used are characterized by the typical problems found in classic data mining applications using real-world data, such as noise and inconsistencies. The two classes are also clearly unbalanced in the data, a problem which is addressed using algorithms that resample the instances. For classification, a series of features are extracted from spatiotemporal data that represent the trajectories of the ships, available from sequences of Automatic Identification System (AIS) reports. These features are proposed for the modelling of ship behavior but, because they do not contain context-related information, the classification can be applied in other scenarios. Experimentation shows that the proposed data preparation process is useful for the presented classification problem. In addition, positive results are obtained using minimal information.

show abstract

Section: Related Workmentioning

confidence: 99%

Architecture for Trajectory-Based Fishing Ship Classification with AIS Data

Pedroche

Amigo

García

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…At present, the traditional fusion of SAR and AIS mainly focuses on position matching. A general method of matching the SAR-detected vessels to their AIS messages is described and optimized with tailored weights for each vessel class in [21]. After the target is matched, the AIS information is applied to improve the recognition ability of SAR image ships as auxiliary information, for example, literature [22] improves ship classification in SAR images by transferring AIS knowledge and [17] uses large datasets of mediumresolution SAR images and AIS data as ground truths to evaluate the efficiency of different ship detectors based on adaptive threshold algorithms.…”

Section: Introductionmentioning

confidence: 99%

Improving the Image Quality of Moving Ships for GF-3NG Based on Simultaneous AIS Information

Zhong

Qiu

et al. 2021

Remote Sensing

View full text Add to dashboard Cite

The synthetic aperture radar (SAR) is an important means of ship surveillance, but the motion of the ship leads to azimuth position offset, false targets, and azimuth defocusing for the spaceborne high-resolution and wide-swath (HRWS) SAR system, causing the degradation of imaging quality. The automatic identification system (AIS) can provide real-time information of the ships, which is an important auxiliary method for ship surveillance. Up to now, the traditional fusion of SAR and AIS mainly has focused on location matching and auxiliary recognition, and the next generation of GaoFen-3 (GF-3NG) satellite is equipped with both a SAR sensor and an AIS sensor to obtain the SAR images and simultaneous AIS information of ships. Consequently, this paper proposes a novel scheme to improve the imaging quality of moving ships for GF-3NG using AIS information. In this paper, through introducing the virtual stationary target, the slant range derivation (SRD) algorithm is proposed to estimate the radial velocity (RV) and the radial acceleration (RA) between the ship and the SAR platform relative to the stationary scene. According to the calculated RV, the azimuth position offset can be estimated and the ship can be repositioned on the image. After that, the traditional method is conducted to suppress the false targets. Finally, the method of using the RA to refocus ship slices is proposed. Additionally, the experiment results based on real data illustrate the effectiveness of the proposed methods.

show abstract