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Proceedings Paper

SAR ATR in the phase history domain using deep convolutional neural networks
Author(s): Muhammed Burak Alver; Sara Atito; Müjdat Çetin
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Paper Abstract

Synthetic aperture radar (SAR) automatic target recognition (ATR) has been an interesting topic of research for decades. Existing methods perform the ATR task after image formation. However, in principle, image formation does not provide any new information regarding the classification task and it may even cause some information loss. Motivated by this, in this paper, we examine two SAR ATR frameworks that work in the phase history domain. In the first framework, we feed the complex-valued phase histories to a deep convolutional neural network (CNN) directly, and in the second one, we perform image formation, phase removal, and phase history generation before feeding the data to the CNN. CNNs are known for their superior performance on image classification tasks. The effectiveness of CNNs is based on dependency patterns in a given input. Thus, the input of CNNs is not limited to images but any input exhibiting such dependencies. Since complex-valued phase histories also have such a structure, they can be the input of a CNN. We perform ATR experiments on the Moving and Stationary Target Acquisition and Recognition (MSTAR) database and compare the results of image-based and phase history-based classification.

Paper Details

Date Published: 9 October 2018
PDF: 10 pages
Proc. SPIE 10789, Image and Signal Processing for Remote Sensing XXIV, 1078913 (9 October 2018); doi: 10.1117/12.2325365
Show Author Affiliations
Muhammed Burak Alver, Sabanci Univ. (Turkey)
Sara Atito, Sabanci Univ. (Turkey)
Müjdat Çetin, Sabanci Univ. (Turkey)
Univ. of Rochester (United States)

Published in SPIE Proceedings Vol. 10789:
Image and Signal Processing for Remote Sensing XXIV
Lorenzo Bruzzone; Francesca Bovolo, Editor(s)

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