Autonomous interpretation of side scan sonar returns

Castellano, Andrea; Gray, Brian

doi:10.1109/auv.1990.110464

Cited by 9 publications

(2 citation statements)

References 8 publications

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“…The authors obtained the classification accuracy rates as follows for valleys, 80.1%, sediment ponds, 85%, ridge flanks, 91%. Anthony R. Castellano Brian C. Gray [4] proposed Neural network algorithm for classification, signal detection and feature is extracted using SSS geometry. The authors worked on real time data identifying bottom objects.…”

Section: Literature Surveymentioning

confidence: 99%

Classification performance assessment in side scan sonar image while underwater target object recognition using random forest classifier and support vector machine

Kumudham¹,

Rajendran²

2018

IJET

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Ocean mine have been a major threat to the safety of vessels and human lives for many years. Identification of mine-like objects is a pressing need for military, and other ocean meets. In mine, countermeasures operations, sonar equipment are utilised to detect and classify mine-like objects if their sonar signatures are similar to known signatures of mines. The classification of underwater mines is an important task, for the safety of ports, harbors and the open sea. Mine detection is needed in military applications because it has been a threat to many lives and vessels. Although the task of finding mine like objects has received recent attention, very little has been published on the problem of discriminating mine-like (target) objects (MLO) and non-mine like (target) objects of similar size and shape. This paper deals with the recognition of mine like and non mine like objects. The recognition is done through robust Random Forest technique.

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Section: Literature Surveymentioning

confidence: 99%

Classification performance assessment in side scan sonar image while underwater target object recognition using random forest classifier and support vector machine

Kumudham¹,

Rajendran²

2018

IJET

View full text Add to dashboard Cite

show abstract

“…In general classical models Castellano & Gray (1990)Quidu et al (2000)Delvigne (1992 consisting of feature extraction and classification have widely been used for mono-view classification purposes. First using a presegmented shadow a mine a set of features are A totally different approach based on available properties of the shape (as a prior model) and an observation model (likehood model) was proposed by Mignotte et al (2000).…”

Section: Mono-view Classificationmentioning

confidence: 99%