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COMPLEX INPUT CONVOLUTIONAL NEURAL NETWORKS FOR WIDE ANGLE SAR ATR

Citation Author(s):
Michael Wilmanski, Chris Kreucher, Alfred Hero
Submitted by:
Michael Wilmanski
Last updated:
11 December 2016 - 2:48pm
Document Type:
Presentation Slides
Document Year:
2016
Event:
Presenters:
Michael Wilmanski
Paper Code:
1341
 

To date, automatic target recognition (ATR) techniques in synthetic aperture radar (SAR) imagery have largely focused on features that use only the magnitude part of SAR’s complex valued magnitude-plus-phase history. While such techniques are often very successful, they inherently ignore the significant amount of discriminatory information available in the phase. This paper describes a method for exploiting the complex information for ATR by using a convolutional neural network (CNN) that accepts fully complex input features. We show a performance leap from 87.30% to 99.21% accuracy on real collected wide-angle SAR data with the use of complex features.

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