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What's Mine is Yours: Pretrained CNNs for Limited Training Sonar ATR
Finding mines in Sonar imagery is a significant problem with a great deal of relevance for seafaring military and commercial endeavors.
Bridging the Gap: Simultaneous Fine Tuning for Data Re-Balancing
There are many real-world classification problems wherein the issue of data imbalance (the case when a data set contains substantially more samples for one/many classes than the rest) is unavoidable.
Additional Representations for Improving Synthetic Aperture Sonar Classification Using Convolutional Neural Networks
For human consumption, the SLC is converted to a magnitude-phase representation and the phase information is discarded.
Measuring Human Assessed Complexity in Synthetic Aperture Sonar Imagery Using the Elo Rating System
The heuristic and information theoretical metrics were then compared to the image rank from which they were derived.
Coupling Rendering and Generative Adversarial Networks for Artificial SAS Image Generation
Acquisition of Synthetic Aperture Sonar (SAS) datasets is bottlenecked by the costly deployment of SAS imaging systems, and even when data acquisition is possible, the data is often skewed towards containing barren seafloor rather than objects of interest.
Deep Autofocus for Synthetic Aperture Sonar
In this letter, we demonstrate the potential of machine learning, specifically deep learning, to address the autofocus problem.
