  Synthetic aperture sonar (SAS) imagery is crucial for several applications,
including target recognition and environmental segmentation. Deep learning
models have led to much success in SAS analysis; however, the features
extracted by these approaches may not be suitable for capturing certain
textural information. To address this problem, we present a novel application
of histogram layers on SAS imagery. The addition of histogram layer(s) within
the deep learning models improved performance by incorporating statistical
texture information on both synthetic and real-world datasets.
