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Advancements in Sonar image capture have enabled researchers to applysophisticated object identification algorithms in order to locate targets ofinterest in images such as mines. Despite progress in this field, modern sonarautomatic target recognition (ATR) approaches lack robustness to the amount ofnoise one would expect in real-world scenarios, the capability to handleblurring incurred from the physics of image capture, and the ability to excelwith relatively few training samples. We address these challenges by adaptingmodern sparsity-based techniques with dictionaries comprising of training fromeach class. We develop new discriminative (as opposed to generative) sparserepresentations which can help automatically classify targets in Sonar imaging.Using a simulated SAS data set from the Naval Surface Warfare Center (NSWC), weobtained compelling classification rates for multi-class problems even in caseswith considerable noise and sparsity in training samples.

Discriminative Sparsity for Sonar ATR