Multi-view SAS image classification using deep learning

Abstract

A new approach is proposed for multi-view classification when sonar data is in the form of imagery and each object has been viewed an arbitrary number of times. An image-fusion technique is employed in conjunction with a deep learning algorithm (based on Boltzmann machines) so that the sonar data from multiple views can be combined and exploited at the (earliest) image level. The method utilizes singleview imagery and, whenever available, multi-view fused imagery, in the same unified classification framework. The promise of the proposed approach is demonstrated in the context of an object classification task with real synthetic aperture sonar (SAS) imagery collected at sea.