Classification of objects in synthetic aperture sonar images (with B. Marchand and H. Xiao), Proceedings of 14th IEEE Statistical Signal Processing Workshop, pp.433-437, 2007.
This paper discusses an approach for the classification of objects in Synthetic
Aperture Sonar (SAS) images and its benefit over other approaches. Our approach
fully utilizes raw sonar waveforms scattered from objects.
To do so, we first locate objects of interest in an image obtained by
SAS processing. Then we extract the portions of the raw sonar waveforms
responsible for forming those imaged objects from the whole raw sonar data.
We align/straighten these extracted waveforms for localized discriminant feature
analysis from which we obtain local features used for classification. We
demonstrate the usefulness of our approach using real experimental sonar data.
Get the full paper: PDF file.
Get the official version via doi:10.1109/SSP.2007.4301295.
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