Omega-KA-Net: A SAR Ground Moving Target Imaging Network Based on Trainable Omega-K Algorithm and Sparse Optimization

Zhang, Hongwei; Ni, Jiacheng; Xiong, Shue; Luo, Ying; Zhang, Qun

doi:10.3390/rs14071664

Cited by 6 publications

(4 citation statements)

References 35 publications

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“…Similarly, a target-oriented SAR imaging network, obtained by unfolding an MF-based ADMM iterative solution, was proposed in [26] to enhance the signal-to-clutter ratio of the desired target. For SAR imaging of moving targets, deep-unfolding-based focusing methods have been proposed to reconstruct sparse ship targets [27,28]. In addition, deep unfolding technology can also be applied to the sparse imaging of ISAR, such as ADMM-Net [29] and AF-AMPNet [30].…”

Section: Previous Workmentioning

confidence: 99%

Nonsparse SAR Scene Imaging Network Based on Sparse Representation and Approximate Observations

Zhang,

Ni,

et al. 2023

Remote Sensing

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Sparse-representation-based synthetic aperture radar (SAR) imaging technology has shown superior potential in the reconstruction of nonsparse scenes. However, many existing compressed sensing (CS) methods with sparse representation cannot obtain an optimal sparse basis and only apply to the sensing matrix obtained by exact observation, resulting in a low image quality occupying more storage space. To reduce the computational cost and improve the imaging performance of nonsparse scenes, we formulate a deep learning SAR imaging method based on sparse representation and approximated observation deduced from the chirp-scaling algorithm (CSA). First, we incorporate the CSA-derived approximated observation model and a nonlinear transform function within a sparse reconstruction framework. Second, an iterative shrinkage threshold algorithm is adopted to solve this framework, and the solving process is unfolded as a deep SAR imaging network. Third, a dual-path convolutional neural network (CNN) block is designed in the network to achieve the nonlinear transform, dramatically improving the sparse representation capability over conventional transform-domain-based CS methods. Last, we improve the CNN block to develop an enhanced version of the deep SAR imaging network, in which all the parameters are layer-varied and trained by supervised learning. The experiments demonstrate that our proposed two imaging networks outperform conventional CS-driven and deep-learning-based methods in terms of computing efficiency and reconstruction performance of nonsparse scenes.

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Section: Previous Workmentioning

confidence: 99%

Nonsparse SAR Scene Imaging Network Based on Sparse Representation and Approximate Observations

Zhang,

Ni,

et al. 2023

Remote Sensing

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“…In theory, neural networks can learn to approximate arbitrary functions and should be able to learn a mapping from raw data to a desired output, given sufficient and representative training data and a suitable model. For example, an end-toend computational pipeline to perform image focusing and landscape classification was proposed [39] or a trainable Omega-K and sparse optimization based SAR-GMT imaging network with improved image quality [40].…”

Section: Introductionmentioning

confidence: 99%

Ship Detection From Raw SAR Echoes Using Convolutional Neural Networks

De Sousa,

Pilikos,

Azcueta

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Synthetic Aperture Radar (SAR) is an indispensable tool for marine monitoring. Conventional data processing involves data down-linking and on-ground operations for image focusing, analysis and ship detection. These steps take significant amount of time, resulting in potentially critical delays. In this work, we propose a ship detection algorithm that operates directly on raw SAR echoes, based on convolutional neural networks. To evaluate our approach, we performed experiments using raw data simulations and real raw SAR data from Sentinel-1 stripmap mode scenes. Preliminary results on this set show the capability of detecting multiple ships from raw data with similar accuracy as using Single-Look-Complex (SLC) images as input. Simultaneously, running time is reduced significantly, by-passing the image focusing step. This illustrates the great potential of deep learning, moving towards more intelligent SAR systems.

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“…With the development of deep learning technology, deep neural networks have been widely utilized in the SAR moving-target-imaging task [16][17][18][19][20][21][22]. In [19], a deep CNN-based method was first explored and applied for multi-moving-target imaging in a SAR-GMTI system.…”

Section: Introductionmentioning

confidence: 99%

“…Besides, in the ship-target-imaging application, a complex-valued channel fusion U-shaped network was designed for ship target refocusing [23], and a complexvalued convolutional autoencoder based on the attention mechanism was proposed to improve the imaging of ship targets in the GEO SA-Bi SAR system [24]. A novel Omega-KA-net based on sparse optimization was proposed to realize moving-target imaging [21]. The high-quality imaging results can be obtained under down-sampling and a low signalto-noise ratio (SNR).…”

Section: Introductionmentioning

confidence: 99%

A Multicomponent Linear Frequency Modulation Signal-Separation Network for Multi-Moving-Target Imaging in the SAR-Ground-Moving-Target Indication System

Ding,

Mu,

Zhang

2024

Remote Sensing

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Multi-moving-target imaging in a synthetic aperture radar (SAR) system poses a significant challenge owing to target defocusing and being contaminated by strong background clutter. Aiming at this problem, a new deep-convolutional-neural-network (CNN)-assisted method is proposed for multi-moving-target imaging in a SAR-GMTI system. The multi-moving-target signal can be modeled by a multicomponent LFM signal with additive perturbation. A fully convolutional network named MLFMSS-Net was designed based on an encoder–decoder architecture to extract the most-energetic LFM signal component from the multicomponent LFM signal in the time domain. Without prior knowledge of the target number, an iterative signal-separation framework based on the well-trained MLFMSS-Net is proposed to separate the multi-moving-target signal into multiple LFM signal components while eliminating the residual clutter. It works well, exhibiting high imaging robustness and low dependence on the system parameters, making it a suitable solution for practical imaging applications. Consequently, a well-focused multi-moving-target image can be obtained by parameter estimation and secondary azimuth compression for each separated LFM signal component. The simulations and experiments on both airborne and spaceborne SAR data showed that the proposed method is superior to traditional imaging methods in both imaging quality and efficiency.

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Omega-KA-Net: A SAR Ground Moving Target Imaging Network Based on Trainable Omega-K Algorithm and Sparse Optimization

Cited by 6 publications

References 35 publications

Nonsparse SAR Scene Imaging Network Based on Sparse Representation and Approximate Observations

Nonsparse SAR Scene Imaging Network Based on Sparse Representation and Approximate Observations

Ship Detection From Raw SAR Echoes Using Convolutional Neural Networks

A Multicomponent Linear Frequency Modulation Signal-Separation Network for Multi-Moving-Target Imaging in the SAR-Ground-Moving-Target Indication System

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