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arXiv · 2209.03878

Histogram Layers for Synthetic Aperture Sonar Imagery

Joshua Peeples·Alina Zare·Jeffrey Dale·James Keller
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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.

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Joshua Peeples, Alina Zare, Jeffrey Dale, James Keller. 2022-09-08. Histogram Layers for Synthetic Aperture Sonar Imagery. https://doi.org/10.1109/icmla55696.2022.00032

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