A comparison of inter-frame feature measures for robust object classification in sector scan sonar image sequences

Ioseba Joaquin Tena Ruiz, David Michael Lane, Mike Chantler

Research output: Contribution to journalArticle

33 Citations (Scopus)

Abstract

This paper presents an investigation of the robustness of an inter-frame feature measure classifier for underwater sector scan sonar image sequences. In the initial stages the images are of either divers or remotely operated vehicles (ROV's). The inter-frame feature measures are derived from sequences of sonar scans to characterize the behavior of the objects over time. The classifier has been shown to produce error rates of 0%-2% using real nonnoisy images. The investigation looks at the robustness of the classifier with increased noise conditions and changes in the filtering of the images. It also identifies a set of features that are less susceptible to increased noise conditions and changes in the image filters. These features are the mean variance, and the variance of the rate of change in time of the intra-frame feature measures area, perimeter, compactness, maximum dimension and the first and second invariant moments of the objects. It is shown how the performance of the classifier can be improved. Success rates of up to 100% were obtained for a classifier trained under normal noise conditions, signal-to-noise ratio (SNR) around 9.5 dB, and a noisy test sequence of SNR 7.6 dB.

Original languageEnglish
Pages (from-to)458-469
Number of pages12
JournalIEEE Journal of Oceanic Engineering
Volume24
Issue number4
DOIs
Publication statusPublished - Oct 1999

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