  This paper details a new method to recognize and detect underwater objects in
real-time sidescan sonar data imagery streams, with case-studies of
applications for underwater archeology, and ghost fishing gear retrieval. We
first synthesize images from sidescan data, apply geometric and radiometric
corrections, then use 2D feature detection algorithms to identify point clouds
of descriptive visual microfeatures such as corners and edges in the sonar
images. We then apply a clustering algorithm on the feature point clouds to
group feature sets into regions of interest, reject false positives, yielding a
georeferenced inventory of objects.
