Deep Learning for Compressive SAR Imaging with Train-Test Discrepancy

Bibliographic Details
Main Author:	McCamey, Morgan R.
Language:	English
Published:	Wright State University / OhioLINK 2021
Subjects:	Computer Engineering Computer Science Electrical Engineering SAR deep learning machine learning compressive sensing CS signal recovery SAR imaging synthetic SAR image recovery compressed SAR compressed signals DNN neural network
Online Access:	http://rave.ohiolink.edu/etdc/view?acc_num=wright1624266549100904

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spelling	ndltd-OhioLink-oai-etd.ohiolink.edu-wright16242665491009042021-08-03T07:17:36Z Deep Learning for Compressive SAR Imaging with Train-Test Discrepancy McCamey, Morgan R. Computer Engineering Computer Science Electrical Engineering SAR deep learning machine learning compressive sensing CS signal recovery SAR imaging synthetic SAR image recovery compressed SAR compressed signals DNN neural network We consider the problem of compressive synthetic aperture radar (SAR) imaging with the goal of reconstructing SAR imagery in the presence of undersampled phase history. While this problem is typically considered in compressive sensing (CS) literature, we consider a variety of deep learning approaches where a deep neural network (DNN) is trained to form SAR imagery from limited data. At the cost of computationally intensive offline training, on-line test-time DNN-SAR has demonstrated orders of magnitude faster reconstruction than standard CS algorithms. A limitation of the DNN approach is that any change to the operating conditions necessitates a costly retraining procedure. In this work, we consider development of DNN methods that are robust to discrepancies between training and testing conditions. We examine several approaches to this problem, including using input-layer dropout, augmented data support indicators, and DNN-based robust approximate message passing. 2021-06-21 English text Wright State University / OhioLINK http://rave.ohiolink.edu/etdc/view?acc_num=wright1624266549100904 http://rave.ohiolink.edu/etdc/view?acc_num=wright1624266549100904 unrestricted This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws.
collection	NDLTD
language	English
sources	NDLTD
topic	Computer Engineering Computer Science Electrical Engineering SAR deep learning machine learning compressive sensing CS signal recovery SAR imaging synthetic SAR image recovery compressed SAR compressed signals DNN neural network
spellingShingle	Computer Engineering Computer Science Electrical Engineering SAR deep learning machine learning compressive sensing CS signal recovery SAR imaging synthetic SAR image recovery compressed SAR compressed signals DNN neural network McCamey, Morgan R. Deep Learning for Compressive SAR Imaging with Train-Test Discrepancy
author	McCamey, Morgan R.
author_facet	McCamey, Morgan R.
author_sort	McCamey, Morgan R.
title	Deep Learning for Compressive SAR Imaging with Train-Test Discrepancy
title_short	Deep Learning for Compressive SAR Imaging with Train-Test Discrepancy
title_full	Deep Learning for Compressive SAR Imaging with Train-Test Discrepancy
title_fullStr	Deep Learning for Compressive SAR Imaging with Train-Test Discrepancy
title_full_unstemmed	Deep Learning for Compressive SAR Imaging with Train-Test Discrepancy
title_sort	deep learning for compressive sar imaging with train-test discrepancy
publisher	Wright State University / OhioLINK
publishDate	2021
url	http://rave.ohiolink.edu/etdc/view?acc_num=wright1624266549100904
work_keys_str_mv	AT mccameymorganr deeplearningforcompressivesarimagingwithtraintestdiscrepancy
_version_	1719458614974873600

Deep Learning for Compressive SAR Imaging with Train-Test Discrepancy

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