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Deep Learning for Compressive SAR Imaging with Train-Test Discrepancy

McCamey, Morgan R.
http://rave.ohiolink.edu/etdc/view?acc_num=wright1624266549100904

Abstract Details

2021, Master of Science in Computer Engineering (MSCE), Wright State University, Computer Engineering.
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.
Joshua Ash, Ph.D. (Advisor)
Tanvi Banerjee, Ph.D. (Committee Member)
Mateen Rizki, Ph.D. (Committee Member)
61 p.
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

Recommended Citations

Citations

  • McCamey, M. R. (2021). Deep Learning for Compressive SAR Imaging with Train-Test Discrepancy [Master's thesis, Wright State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=wright1624266549100904

    APA Style (7th edition)

  • McCamey, Morgan. Deep Learning for Compressive SAR Imaging with Train-Test Discrepancy. 2021. Wright State University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=wright1624266549100904.

    MLA Style (8th edition)

  • McCamey, Morgan. "Deep Learning for Compressive SAR Imaging with Train-Test Discrepancy." Master's thesis, Wright State University, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=wright1624266549100904

    Chicago Manual of Style (17th edition)

wright1624266549100904
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This open access ETD is published by Wright State University and OhioLINK.
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