Statistically Principled Deep Learning for SAR Image Segmentation

Authors

  • Cassandra Goldberg Bowdoin College

DOI:

https://doi.org/10.1609/aaai.v38i21.30549

Keywords:

SAR Imaging, Deep Learning, Image Segmentation, Statistics, Remote Sensing

Abstract

This paper proposes a novel approach for Synthetic Aperture Radar (SAR) image segmentation by incorporating known statistical properties of SAR into deep learning models. We generate synthetic data using the Generalized Gamma distribution, modify the U-Net architecture to encompass statistical moments, and employ stochastic distance losses for improved segmentation performance. Evaluation against traditional methods will reveal the potential of this approach to advance SAR image analysis, with broader applications in environmental monitoring and general image segmentation tasks.

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Published

2024-03-24

How to Cite

Goldberg, C. (2024). Statistically Principled Deep Learning for SAR Image Segmentation. Proceedings of the AAAI Conference on Artificial Intelligence, 38(21), 23742-23743. https://doi.org/10.1609/aaai.v38i21.30549