A comparison of inter-frame feature measures for robust object classification
in sector scan sonar image sequences
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 image. 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.
Author(s): I. Tena Ruiz, D. M. Lane, M. J. Chantler
Publication: IEEE Journal of Oceanic Engineering
Issue: Vol. 24, No. 4, October 1999
Page no.: 458 - 469