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-...

Full description

Saved in:

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
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!