Development of CNNs for feature extraction

Nicole Eikmeier; Rachel Westerkamp; Ed Zelnio

doi:10.1117/12.2305394

27 April 2018 Development of CNNs for feature extraction

Nicole Eikmeier, Rachel Westerkamp, Ed Zelnio

Proceedings Volume 10647, Algorithms for Synthetic Aperture Radar Imagery XXV; 106470C (2018) https://doi.org/10.1117/12.2305394
Event: SPIE Defense + Security, 2018, Orlando, FL, United States

Abstract

There are significant challenges in applying deep learning technology to classifying targets. Among the challenges in deep learning algorithms, limited amount of measured data makes classification of targets using synthetic aperture radar very difficult. Our approach is to use CNNs to extract feature level information. We explore both regression and classification of features, and achieve accurate results in estimating the target’s azimuth angle while using testing and training sets that have no overlap in target types. We introduce dropout into the network architecture to capture confidence in our algorithmic output, with the future goal of confidence across multi-sensor feature-level classification.

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Citation Download Citation

Nicole Eikmeier, Rachel Westerkamp, and Ed Zelnio "Development of CNNs for feature extraction", Proc. SPIE 10647, Algorithms for Synthetic Aperture Radar Imagery XXV, 106470C (27 April 2018); https://doi.org/10.1117/12.2305394

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