SAR moving target imaging based on convolutional neural network

Lu, Zhijun; Qin, Qi; Shi, Hongyin; Huang, Hao

doi:10.1016/j.dsp.2020.102832

Cited by 16 publications

(10 citation statements)

References 15 publications

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“…SAR deep learning imaging networks can be used for unsupervised training or supervised training [30] by using the known feature information of SAR target geometry size, shape, and statistical distribution. As the SAR imaging scene is unknown, it is impossible to directly use the scattering coefficient of the scene for error backpropagation.…”

Section: Training the Deep Learning Imaging Networkmentioning

confidence: 99%

End-to-End SAR Deep Learning Imaging Method Based on Sparse Optimization

Zhao

Liang

et al. 2021

Remote Sensing

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Synthetic aperture radar (SAR) imaging has developed rapidly in recent years. Although the traditional sparse optimization imaging algorithm has achieved effective results, its shortcomings are slow imaging speed, large number of parameters, and high computational complexity. To solve the above problems, an end-to-end SAR deep learning imaging algorithm is proposed. Based on the existing SAR sparse imaging algorithm, the SAR imaging model is first rewritten to the SAR complex signal form based on the real-value model. Second, instead of arranging the two-dimensional echo data into a vector to continuously construct an observation matrix, the algorithm only derives the neural network imaging model based on the iteration soft threshold algorithm (ISTA) sparse algorithm in the two-dimensional data domain, and then reconstructs the observation scene through the superposition and expansion of the multi-layer network. Finally, through the experiment of simulation data and measured data of the three targets, it is verified that our algorithm is superior to the traditional sparse algorithm in terms of imaging quality, imaging time, and the number of parameters.

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Section: Training the Deep Learning Imaging Networkmentioning

confidence: 99%

End-to-End SAR Deep Learning Imaging Method Based on Sparse Optimization

Zhao

Liang

et al. 2021

Remote Sensing

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“…In recent years, researchers have explored the use of deep neural networks in SAR image refocusing [ 18 , 19 , 20 ]. Lu et al used an improved U-net convolutional neural network for refocusing [ 21 ]. Image quality was improved in the case of large azimuth velocity, and the image resolution was also improved.…”

Section: Introductionmentioning

confidence: 99%

Hardware Accelerated Design of a Dual-Mode Refocusing Algorithm for SAR Imaging Systems

2023

Sensors

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The use of satellite synthetic aperture radar (SAR) for moving target imaging has gained popularity recently. Researchers are focused on improving its imaging quality. To achieve high-quality and fast imaging, we have developed a dual-mode refocusing algorithm. We optimized the algorithm’s target speed estimation and carried out data enhancement and quantization design for SAR image refocusing. The design is implemented on a Xilinx XC5VFX130T FPGA. The dual-mode image data are based on a slice size of 512 × 512 for slice mode and 256 × 256 for scan mode in a time-series function simulation. The serial–parallel conversion and pipeline design balances the operating speed and logic resources for optimal performance. Experiment results on slice data of real SAR images show that the system’s processing speed can reach two frames per second, utilizing 69633 LUTs, 255 RAMs, and 296 DSPs.

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“…However, these low-level image feature extraction methods have limited effect on extracting lung image information. In [5][6] convolutional neural network (CNN) is widely used in image classification due to its excellent performance in the field of computer vision provide good classification accuracy. At the same time, the attention mechanism has also become a major factor in improving the classification performance.…”

Section: Introductionmentioning

confidence: 99%

Lung Image Classification Based On Long-Short Term Memory recurrent neural network

Shaker

Xiong

2023

J. Phys.: Conf. Ser.

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Due to various factors such as complicated lung imaging and rapidly growing amount of data, the task for imaging technicians is arduous. The emergence of artificial intelligence assisted diagnosis technology comes just in time.To effectively classify lung partial images and alleviate the burden of medical application, a deep learning method based on attention mechanism is therefore developed. The proposed model uses deep learning as the basic integrates Long-Shot term memory (LSTM) the recurrent neural network (RNN). Technology for lung imaging diagnosis based on artificial intelligence has evolved through time from combined diagnosis of multiple diseases to the diagnosis of a single specific disease. The suggested network’s overall classification accuracy, according to experiments, is 95.93%, which is 1.019% greater than that of the deep learning basic network. It also outperforms the VGG16 and VGG19 networks in terms of classification performance. Finally, the benefits and drawbacks of the suggested algorithm are explored, as well as the future development path.

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SAR moving target imaging based on convolutional neural network

Cited by 16 publications

References 15 publications

End-to-End SAR Deep Learning Imaging Method Based on Sparse Optimization

End-to-End SAR Deep Learning Imaging Method Based on Sparse Optimization

Hardware Accelerated Design of a Dual-Mode Refocusing Algorithm for SAR Imaging Systems

Lung Image Classification Based On Long-Short Term Memory recurrent neural network

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