SAE-Net: A Deep Neural Network for SAR Autofocus

The sparsity-driven technique is a widely used tool to solve the synthetic aperture radar (SAR) imaging problem. However, it always encounters sensitivity to motion errors. To solve this problem, this article proposes a new deep neural network architecture, i.e., the sparse autoencoder network (SAE-...

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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing Vol. 60; pp. 1 - 14
Main Author: Pu, Wei
Format: Journal Article
Language:English
Published: New York IEEE 2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Artificial neural networks
Autoencoder
autofocus
Coders
Computer architecture
Deep learning
deep neural network
Echoes
Entropy
Imaging
Imaging techniques
Neural networks
Radar imaging
Radar polarimetry
SAR (radar)
sparse autoencoder network (SAE-Net)
Sparsity
Synthetic aperture radar
synthetic aperture radar (SAR)
Training
Transforms
ISSN:0196-2892, 1558-0644
Online Access:Get full text
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Summary:The sparsity-driven technique is a widely used tool to solve the synthetic aperture radar (SAR) imaging problem. However, it always encounters sensitivity to motion errors. To solve this problem, this article proposes a new deep neural network architecture, i.e., the sparse autoencoder network (SAE-Net). The proposed SAE-Net is designed to implement SAR imaging and autofocus simultaneously. In SAE-Net, the encoder transforms the SAR echo into an imaging result, and the decoder regenerates the SAR echo using the obtained imaging result. The encoder is designed by the unfolded alternating direction method of multipliers (ADMM), while the decoder is formulated into a linear mapping. The joint reconstruction loss and the entropy loss are utilized to guide the training of the SAE-Net. Notably, the algorithm operates in a totally self-supervised form and requires no other training dataset. The methodology was tested on both synthetic and real SAR data. These tests show that the proposed architecture outperforms other state-of-the-art autofocus methods in sparsity-driven SAR imaging applications.
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ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2021.3139914