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Proceedings Paper

Development of CNNs for feature extraction
Author(s): Nicole Eikmeier; Rachel Westerkamp; Ed Zelnio
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Paper 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.

Paper Details

Date Published: 27 April 2018
PDF: 14 pages
Proc. SPIE 10647, Algorithms for Synthetic Aperture Radar Imagery XXV, 106470C (27 April 2018); doi: 10.1117/12.2305394
Show Author Affiliations
Nicole Eikmeier, Purdue Univ. (United States)
Rachel Westerkamp, Illinois Wesleyan Univ. (United States)
Ed Zelnio, Air Force Research Lab. (United States)


Published in SPIE Proceedings Vol. 10647:
Algorithms for Synthetic Aperture Radar Imagery XXV
Edmund Zelnio; Frederick D. Garber, Editor(s)

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