Histogram Layers for Synthetic Aperture Sonar Imagery
arXiv:2209.03878 · doi:10.1109/ICMLA55696.2022.00032
Abstract
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.
7 pages, 9 Figures, Accepted to IEEE International Conference on Machine Learning and Applications (ICMLA) 2022