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.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.