Peer-Reviewed Technical Communication Semisynthetic Versus Real-World Sonar Training Data for the Classification of Mine-Like Objects

作者： Serge Belongie , Christopher Barngrover , Ryan Kastner

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摘要: The detection of mine-likeobjects (MLOs)in sidescan sonar (SSS) imagery continues to be a challenging task. In prac- tice, subject matter experts tediously analyze images searching for MLOs. the literature, there are many attempts at automated targetrecognition(ATR)todetecttheMLOs.Thispaperfocuseson classifiers that use computer vision and machine learning ap- proaches.Thesetechniquesrequirelargeamountsof data,which is often prohibitive. For this reason, synthetic semisyn- theticdatasetsfortrainingandtestingiscommonplace.Thispaper shows how simple semisynthetic data creation scheme can used pretest these data-hungry training algorithms determine what features value. paper provides real-world testing sets in addition sets. considers Haar-like local bi- nary pattern (LBP) with boosting, showing improvements performance real over as set size increases.

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Peer-Reviewed Technical Communication Semisynthetic Versus Real-World Sonar Training Data for the Classification of Mine-Like Objects

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Peer-Reviewed Technical Communication Semisynthetic Versus Real-World Sonar Training Data for the Classification of Mine-Like Objects

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