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Optimal Feature Set for Automatic Detection and Classification of Underwater Objects in SAS Images

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2 Author(s)
Fandos, R. ; Signal Process. Group, Tech. Univ. Darmstadt, Darmstadt, Germany ; Zoubir, A.M.

The problem of automatic detection and classification for mine hunting applications is addressed. We propose a set of algorithms which are tested using a large database of real synthetic aperture sonar (SAS) images. The highlights and shadows of the objects in an SAS image are segmented using both a Markovian algorithm and the active contours algorithm. The comparison of both segmentation results is used as a feature for classification. In addition, other features are considered. These include geometrical shape descriptors, not only of the shadow region, but also of the object highlight, which demonstrates a significant improvement of the performance. Furthermore, a novel set of features based on the image statistics is described. Finally, we propose an optimal feature set that leads to the best classification results for the available database.

Published in:

Selected Topics in Signal Processing, IEEE Journal of  (Volume:5 ,  Issue: 3 )

Date of Publication:

June 2011

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