Histogram Layers for Synthetic Aperture Sonar Imagery

TL;DR

This paper introduces histogram layers into deep learning models for synthetic aperture sonar imagery to enhance texture feature extraction, leading to improved analysis performance on synthetic and real datasets.

Contribution

It presents a novel integration of histogram layers into deep learning models specifically for SAS imagery, improving texture feature representation.

Findings

Histogram layers improve model performance on SAS tasks.

Enhanced texture information benefits target recognition and segmentation.

Method shows effectiveness on both synthetic and real-world datasets.

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.