Classifying ships using low resolution maritime radar

Gibbins, Danny; Gray, Doug; Dempsey, D. J.

doi:10.1109/isspa.1999.818178

Cited by 8 publications

(8 citation statements)

References 6 publications

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“…Musman et al (1996) classified ships using local features of radar images, with wire-frame models of ship types, but claims the images were very different from visual ones. Gibbins et al (1999) classified radar images of ships in using global features but their accuracy is so far unimpressive. Gouaillier and Gagnon (1997) classified radar images using principal-component global features and similarity matching to prototypical ships for each ship type to obtain promising results.…”

Section: Introductionmentioning

confidence: 99%

Robust Recognition of Ship Types from an Infrared Silhouette

Alves¹,

Herman²,

Rowe

2004

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Accurate identification of unknown contacts crucial in military intelligence. Automated systems that quickly and accurately determine the identity of a contact could be a benefit in backing up electronic-signals identification methods. This work reports two experimental systems for ship classification from infrared FLIR images. In an edge-histogram approach, we used the histogram of the binned distribution of observed straight edge segments of the ship image. Some simple tests had a classification success rate of 80% on silhouettes. In a more comprehensive neuralnetwork approach, we calculated scale-invariant moments of a silhouette and used them as input to a neural network. We trained the network on several thousand perspectives of a wire-frame model of the outline of each of five ship classes. We obtained 70% accuracy with detailed tested on real infrared images but performance was more robust than with the edge-histogram approach.

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Section: Introductionmentioning

confidence: 99%

Robust Recognition of Ship Types from an Infrared Silhouette

Alves¹,

Herman²,

Rowe

2004

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“…However, with increasing maritime problems by vessels and increasing SAR resolution in recent years, need has been raised by coast guards and related organizations for ship identification, which initiated the research and development of integrated ship detection and identification systems. There are three main approaches to ship identification: (1) Decision-based classification [544][545][546]; (2) Classification by pattern matching [547]; and (3) Multi-channel classification [516,[548][549][550][551][552], excluding the use of ISAR (Inverse SAR) [553]. Probably, the most practical algorithm at present may be the decision-based classification by GMV Aerospace and Defense in Spain [546].…”

Section: Ship Detection and Identificationmentioning

confidence: 99%

Recent Trend and Advance of Synthetic Aperture Radar with Selected Topics

2013

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The present article is an introductory paper in this special issue on synthetic aperture radar (SAR). A short review is presented on the recent trend and development of SAR and related techniques with selected topics, including the fields of applications, specifications of airborne and spaceborne SARs, and information contents in and interpretations of amplitude data, interferometric SAR (InSAR) data, and polarimetric SAR (PolSAR) data. The review is by no means extensive, and as such only brief summaries of of each selected topics and key references are provided. For further details, the readers are recommended to read the literature given in the references theirin.

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“…A particularly large body of literature exists for classification using SAR imagery (Greidanus, 2008(Greidanus, , 2005Arnesen and Olsen, 2004). Classification using low resolution radar has also been considered in (Gibbins et al, 1999). Several approaches to vessel classification using infra-red imagery have been published (Teutsch and Kruger, 2010;Alves et al, 2004;Li and Wang, 2008).…”

Section: Maritime Piracy Applicationsmentioning

confidence: 99%

A unified model for context-based behavioural modelling and classification

Dabrowski

Villiers

2015

Expert Systems with Applications

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A unified Bayesian model that simultaneously performs behavioural modelling, information fusion and classification is presented. The model is expressed in the form of a dynamic Bayesian network (DBN). Behavioural modelling is performed by tracking the continuous dynamics of a entity and incorporating various contextual elements that influence behaviour. The entity is classified according to its behaviour. Classification is expressed as a conditional probability of the entity class given its tracked trajectory and the contextual elements. Inference in the DBN is performed using a derived Gaussian sum filter. The model is applied to classify vessels, according to their behaviour, in a maritime piracy situation. The novel aspects of this work include the unified approach to behaviour modelling and classification, the way in which contextual information is fused, the unique approach to classification according to behaviour and the associated derived Gaussian sum filter inference algorithm.

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Classifying ships using low resolution maritime radar

Cited by 8 publications

References 6 publications

Robust Recognition of Ship Types from an Infrared Silhouette

Robust Recognition of Ship Types from an Infrared Silhouette

Recent Trend and Advance of Synthetic Aperture Radar with Selected Topics

A unified model for context-based behavioural modelling and classification

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