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Iterative, Deep Synthetic Aperture Sonar Image Segmentation
IDUS is an unsupervised learning framework that can be divided into four main steps: 1) A deep network estimates class assignments.
Iterative, Deep, and Unsupervised Synthetic Aperture Sonar Image Segmentation
Our results show that the performance of our proposed method is considerably better than current state-of-the-art methods in SAS image segmentation.
Real-Time, Deep Synthetic Aperture Sonar (SAS) Autofocus
To improve convergence, a hand-crafted weighting function to remove "bad" areas of the image is sometimes applied to the image-under-test before the optimization procedure.
GPU Acceleration for Synthetic Aperture Sonar Image Reconstruction
Finally, we measure ASASIN's performance on a variety of GPUs and create a model capable of predicting performance.
Structural Prior Driven Regularized Deep Learning for Sonar Image Classification
Deep learning has been recently shown to improve performance in the domain of synthetic aperture sonar (SAS) image classification.
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