Target Detection and Segmentation in Circular-Scan Synthetic Aperture Sonar Images Using Semisupervised Convolutional Encoder–Decoders

@article{Sledge2021TargetDA,
  title={Target Detection and Segmentation in Circular-Scan Synthetic Aperture Sonar Images Using Semisupervised Convolutional Encoder–Decoders},
  author={Isaac J. Sledge and Matthew S. Emigh and Jonathan Lee King and Denton L. Woods and James Tory Cobb and Jos{\'e} Carlos Pr{\'i}ncipe},
  journal={IEEE Journal of Oceanic Engineering},
  year={2021},
  volume={47},
  pages={1099-1128}
}
In this article, we propose a framework for saliency-based multitarget detection and segmentation of circular-scan synthetic aperture sonar (CSAS) imagery. Our framework relies on a multibranch convolutional encoder–decoder network (MB-CEDN). The encoder portion of the MB-CEDN extracts visual contrast features from CSAS images. These features are fed into dual decoders that perform pixel-level segmentation to mask targets. Each decoder provides different perspectives as to what constitutes a… 
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External-Memory Networks for Low-Shot Learning of Targets in Forward-Looking-Sonar Imagery

A memory-based framework for real-time, data-efficient target analysis in forward-looking-sonar (FLS) imagery relies on first removing non-discriminative details from the imagery using a small-scale DENSENET-inspired network, which cascades into a novel NEURALRAM-based convolutional matching network, NRMN, for low-shot target recognition.

An Overview of Advances in Signal Processing Techniques for Classical and Quantum Wideband Synthetic Apertures

This guide is intended to aid newcomers in navigating the most critical issues in SA analysis and further supports the development of new theories in the field.

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