CIST: An Improved ISAR Imaging Method Using Convolution Neural Network

Compressive sensing (CS) has been widely utilized in inverse synthetic aperture radar (ISAR) imaging, since ISAR measured data are generally non-completed in cross-range direction, and CS-based imaging methods can obtain high-quality imaging results using under-sampled data. However, the traditional...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2020-08, Vol.12 (16), p.2641
Main Authors: Wei, Shunjun, Liang, Jiadian, Wang, Mou, Zeng, Xiangfeng, Shi, Jun, Zhang, Xiaoling
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
compressive sensing (CS)
Computational efficiency
Computer applications
Computer simulation
Computing costs
Convolution
convolution neural network-iterative shrinkage-thresholding algorithm (CIST)
Fourier transforms
Inverse synthetic aperture radar
inverse synthetic aperture radar (ISAR)
Iterative methods
Learning
Methods
Neural networks
Noise
Parameters
Radar imaging
Remote sensing
Space surveillance
Sparsity
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Summary:Compressive sensing (CS) has been widely utilized in inverse synthetic aperture radar (ISAR) imaging, since ISAR measured data are generally non-completed in cross-range direction, and CS-based imaging methods can obtain high-quality imaging results using under-sampled data. However, the traditional CS-based methods need to pre-define parameters and sparse transforms, which are tough to be hand-crafted. Besides, these methods usually require heavy computational cost with large matrices operation. In this paper, inspired by the adaptive parameter learning and rapidly reconstruction of convolution neural network (CNN), a novel imaging method, called convolution iterative shrinkage-thresholding (CIST) network, is proposed for ISAR efficient sparse imaging. CIST is capable of learning optimal parameters and sparse transforms throughout the CNN training process, instead of being manually defined. Specifically, CIST replaces the linear sparse transform with non-linear convolution operations. This new transform and essential parameters are learnable end-to-end across the iterations, which increases the flexibility and robustness of CIST. When compared with the traditional state-of-the-art CS imaging methods, both simulation and experimental results demonstrate that the proposed CIST-based ISAR imaging method can obtain imaging results of high quality, while maintaining high computational efficiency. CIST-based ISAR imaging is tens of times faster than other methods.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs12162641