Understanding the synthetic and measured GAP from the CNN classifier perspective

Anne Major; Edmund Zelnio; Fred Garber

doi:10.1117/12.2624107

31 May 2022 Understanding the synthetic and measured GAP from the CNN classifier perspective

Anne Major, Edmund Zelnio, Fred Garber

Proceedings Volume 12095, Algorithms for Synthetic Aperture Radar Imagery XXIX; 120950A (2022) https://doi.org/10.1117/12.2624107
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Abstract

Many classifier problems are limited by the amount of data that is available to train the algorithm. This problem is exascerbated by deep learners who require large amounts of data. One approach to solving this problem is to use synthetic data to train the classifier; however, as is often the case, there are differences between synthetic and measured data that limit the performance of this approach. This effort baselines a fundamental approach to solving this problem by using a simple Siamese network to classify the target with one twin trained with abundant synthetic SAR data and the other twin trained with limited measured SAR data. The network is trained using the standard cross-entropy cost function and is the functional equivalent of a single network jointly trained by measured and synthetic data. The performance of this approach is characterized as a function of the amount of measured data required to train the algorithm.

Conference Presentation

Citation Download Citation

Anne Major, Edmund Zelnio, and Fred Garber "Understanding the synthetic and measured GAP from the CNN classifier perspective", Proc. SPIE 12095, Algorithms for Synthetic Aperture Radar Imagery XXIX, 120950A (31 May 2022); https://doi.org/10.1117/12.2624107

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