Hybrid generative and contrastive approaches to the synthetic-measured gap

Jackson S. Zaunegger; Edmund Zelnio

doi:10.1117/12.3014263

7 June 2024 Hybrid generative and contrastive approaches to the synthetic-measured gap

Jackson S. Zaunegger, Edmund Zelnio

Proceedings Volume 13032, Algorithms for Synthetic Aperture Radar Imagery XXXI; 130320P (2024) https://doi.org/10.1117/12.3014263
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Abstract

When performing automatic target recognition it is common to train models using synthetically generated data. This is because synthetically generated data is plentiful, and cheap to produce. Once trained on synthetic data machine learning models are often testing on measured or real-world SAR images. These models do not perform as well when analyzing measured SAR images. This problem is known as the synthetic-measured gap. In this work we explore training generative and contrastive models to close this gap. We train our models on synthetically generated data with the goal of being able to classify measured SAR images. We utilize segmentation masks as well fully-formed SAR images. In the generative approach we explore using an auto-encoder to generate segmentation masks of input SAR images. The auto-encoders architecture includes a classifier which is trained using shared features between the raw image and the segmentation mask. This model is capable of generating a segmentation mask from a SAR image. The contrastive approach uses the Sim-Siam architecture, which utilizes segmentation masks and SAR images. The contrastive model makes a classification decision, by learning features that are shared between the two input types. The goal of this work is to improve classification performance when training on synthetic data, and evaluating on measured data.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jackson S. Zaunegger and Edmund Zelnio "Hybrid generative and contrastive approaches to the synthetic-measured gap", Proc. SPIE 13032, Algorithms for Synthetic Aperture Radar Imagery XXXI, 130320P (7 June 2024); https://doi.org/10.1117/12.3014263

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