Statistically Principled Deep Learning for SAR Image Segmentation

This project explores novel approaches for Synthetic Aperture Radar (SAR) image segmentation that integrate established statistical properties of SAR into deep learning models. First, Perlin Noise and Generalized Gamma distribution sampling methods were utilized to generate a synthetic dataset that effectively captures the statistical attributes of SAR data. Subsequently, deep learning segmentation architectures were developed that utilize average pooling and 1x1 convolutions to perform statistical moment computations. Finally, supervised and unsupervised disparity-based losses were incorporated into model training. The experimental outcomes yielded promising results: the synthetic dataset effectively trained deep learning models for real SAR data segmentation, the statistically-informed architectures demonstrated comparable or superior performance to benchmark models, and the unsupervised disparity-based loss facilitated the delineation of regions within the SAR data. These findings indicate that employing statistically-informed deep learning methodologies could enhance SAR image analysis, with broader implications for various remote sensing applications and the general field of computer vision. The code developed for this project can be found here: https://github.com/cgoldber/Statistically-Principled-SAR-Segmentation.git.