  Advancements in Sonar image capture have enabled researchers to apply
sophisticated object identification algorithms in order to locate targets of
interest in images such as mines. Despite progress in this field, modern sonar
automatic target recognition (ATR) approaches lack robustness to the amount of
noise one would expect in real-world scenarios, the capability to handle
blurring incurred from the physics of image capture, and the ability to excel
with relatively few training samples. We address these challenges by adapting
modern sparsity-based techniques with dictionaries comprising of training from
each class. We develop new discriminative (as opposed to generative) sparse
representations which can help automatically classify targets in Sonar imaging.
Using a simulated SAS data set from the Naval Surface Warfare Center (NSWC), we
obtained compelling classification rates for multi-class problems even in cases
with considerable noise and sparsity in training samples.
