SAR object classification implementation for embedded platforms

Chris Capraro; Uttam Majumder; Josh Siddall; Eric K. Davis; Dan Brown; Chris Cicotta

doi:10.1117/12.2520625

11 June 2019 SAR object classification implementation for embedded platforms

Chris Capraro, Uttam Majumder, Josh Siddall, Eric K. Davis, Dan Brown, Chris Cicotta

Proceedings Volume 10987, Algorithms for Synthetic Aperture Radar Imagery XXVI; 109870F (2019) https://doi.org/10.1117/12.2520625
Event: SPIE Defense + Commercial Sensing, 2019, Baltimore, MD, United States

Abstract

This research details a new approach to optimize neural network architectures for Synthetic Aperture Radar (SAR) object classification on neuromorphic (e.g., IBM’s TrueNorth) and embedded platforms. We developed an algorithm to reduce the run-time and the power consumption of Deep Neural Networks (DNNs) classifiers by reducing the DNN model size required for a given object classification task. Reducing the model size reduces the number of mathematical operations performed, and the memory required, enabling computation on low size, weight and power (SWaP) hardware. We will provide our approach and results on relevant SAR data. Our entirely new approach starts with a very small multi-class convolution neural network (CNN) and replaces the standard negative log likelihood loss function with a single-class log loss function. We then generate an ensemble of small models trained for an individual class by varying the training data using a k-fold cross-validation and augmentation. This is done for each class and the resulting ensembles classify objects by finding the maximum average probability across each ensemble of single-class classifiers. We demonstrate 91-99 percent classification accuracy on three different datasets with composite networks that require almost 10 times fewer mathematical operations than SqueezeNet (a reduced parameter CNN with AlexNet performance).

Conference Presentation

Citation Download Citation

Chris Capraro, Uttam Majumder, Josh Siddall, Eric K. Davis, Dan Brown, and Chris Cicotta "SAR object classification implementation for embedded platforms", Proc. SPIE 10987, Algorithms for Synthetic Aperture Radar Imagery XXVI, 109870F (11 June 2019); https://doi.org/10.1117/12.2520625

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