Classification of Objects in Synthetic Aperture Sonar Images

Bradley Marchand, Naoki Saito, Hong Xiao
2007 2007 IEEE/SP 14th Workshop on Statistical Signal Processing  
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
more » ... ms 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.
doi:10.1109/ssp.2007.4301295 fatcat:xg236cmcunf7lkleud6hu3zrcy