Automatic Target Recognition for Synthetic Aperture Radar Images Based on Super-Resolution Generative Adversarial Network and Deep Convolutional Neural Network

Shi, Xiaoran; Zhou, Feng; Yang, Shuang; Zhang, Zijing; Su, Tao

doi:10.3390/rs11020135

Cited by 32 publications

(17 citation statements)

References 31 publications

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“…An improved method for ATR of SAR images is based on a super-resolution generative adversarial network (SRGAN) and deep convolution neural network (DCNN) (Shi et al, 2019). The approach utilized threshold segmentation to eliminate the SAR image background clutter and speckle noise and accurately extract the target area of interest.…”

Section: Related Workmentioning

confidence: 99%

Multi-Class Dual- Stream Convolutional Neural Network for Synthetic Aperture Radar Automatic Target Recognition of Ground Military Vehicle

Awujoola¹,

Odion²,

Evwiekpaefe³

et al. 2022

JSR

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Synthetic Aperture Radar (SAR) target classification is one of the largest branches of SAR image analysis. Despite the remarkable achievements of deep learning-based SAR target prediction algorithms, current object recognition algorithms are limited in terms of military applications. Acquisition and labeling of SAR target images are time-consuming and cumbersome. Obtaining adequate training data is also challenging in many cases. Deep learning-based models are always susceptible to overfitting because of insufficient training data. This limitation prevents them from being widely used to classify SAR targets. To overcome the problem of insufficient sampling and to learn more accurate representations for SAR image recognition, we propose a two way input of SAR images into a dual stream of DCNN. Concatenating two input SAR images representations was done using the restricted raw SAR data in order to extract the integral features from the 2 input SAR images representations for classification. The proposed methodology addressed the problem of insufficient sample in SAR target classification and improved classification accuracy without overfitting. Experimental results confirmed that the proposed method is effective in addressing the problem of insufficient sample in SAR target classification. This technique can be integrated into any SAR classification model based on convolutional neural networks (CNNs). The model MCDS-CNN results in a 99.1% recognition accuracy. Despite the limited availability of SAR image data from MSTAR, this approach provides good recognition results.

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Section: Related Workmentioning

confidence: 99%

Multi-Class Dual- Stream Convolutional Neural Network for Synthetic Aperture Radar Automatic Target Recognition of Ground Military Vehicle

Awujoola¹,

Odion²,

Evwiekpaefe³

et al. 2022

JSR

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“…Li used it in textile images to restore rich textures [ 43 ]. Xiaoran combined SRGAN and DCNN to achieve super-resolution reconstructed synthetic aperture radar (SAR) images, so as to achieve the purpose of accurately extracting the target area to achieve automatic signal testing [ 44 ]. Sood used SRGAN, SRCNN, SRResNet, and Sparse Representation models for magnetic resonance (MR) resolution improvement respectively.…”

Section: Introductionmentioning

confidence: 99%

Classification of COVID-19 pneumonia from chest CT images based on reconstructed super-resolution images and VGG neural network

Tan

Liu

et al. 2021

Health Inf Sci Syst

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The COVID-19 coronavirus has spread rapidly around the world and has caused global panic. Chest CT images play a major role in confirming positive COVID-19 patients. The computer aided diagnosis of COVID-19 from CT images based on artificial intelligence have been developed and deployed in some hospitals. But environmental influences and the movement of lung will affect the image quality, causing the lung parenchyma and pneumonia area unclear in CT images. Therefore, the performance of COVID-19’s artificial intelligence diagnostic algorithm is reduced. If chest CT images are reconstructed, the accuracy and performance of the aided diagnostic algorithm may be improved. In this paper, a new aided diagnostic algorithm for COVID-19 based on super-resolution reconstructed images and convolutional neural network is presented. Firstly, the SRGAN neural network is used to reconstruct super-resolution images from original chest CT images. Then COVID-19 images and Non-COVID-19 images are classified from super-resolution chest CT images by VGG16 neural network. Finally, the performance of this method is verified by the pubic COVID-CT dataset and compared with other aided diagnosis methods of COVID-19. The experimental results show that improving the data quality through SRGAN neural network can greatly improve the final classification accuracy when the data quality is low. This proves that this method can obtain high accuracy, sensitivity and specificity in the examined test image datasets and has similar performance to other state-of-the-art deep learning aided algorithms.

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“…The extracted features were used by a DNN model to predict the location. An automatic target recognition method for SAR images was developed based on a super-resolution generative adversarial network (SRGAN) and deep convolutional neural network (DCNN) [ 39 ]. The approach was able to suppress background clutter, enhance target feature characterization ability, and achieve automatic target classification and recognition.…”

Section: Introductionmentioning

confidence: 99%

Target Classification in Synthetic Aperture Radar Images Using Quantized Wavelet Scattering Networks

Raj

Fox

Narayanan

2021

Sensors

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The need to classify targets and features in high-resolution imagery is of interest in applications such as detection of landmines in ground penetrating radar and tumors in medical ultrasound images. Convolutional neural networks (CNNs) trained using extensive datasets are being investigated recently. However, large CNNs and wavelet scattering networks (WSNs), which share similar properties, have extensive memory requirements and are not readily extendable to other datasets and architectures—and especially in the context of adaptive and online learning. In this paper, we quantitatively study several quantization schemes on WSNs designed for target classification using X-band synthetic aperture radar (SAR) data and investigate their robustness to low signal-to-noise ratio (SNR) levels. A detailed study was conducted on the tradeoffs involved between the various quantization schemes and the means of maximizing classification performance for each case. Thus, the WSN-based quantization studies performed in this investigation provide a good benchmark and important guidance for the design of quantized neural networks architectures for target classification.

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Automatic Target Recognition for Synthetic Aperture Radar Images Based on Super-Resolution Generative Adversarial Network and Deep Convolutional Neural Network

Cited by 32 publications

References 31 publications

Multi-Class Dual- Stream Convolutional Neural Network for Synthetic Aperture Radar Automatic Target Recognition of Ground Military Vehicle

Multi-Class Dual- Stream Convolutional Neural Network for Synthetic Aperture Radar Automatic Target Recognition of Ground Military Vehicle

Classification of COVID-19 pneumonia from chest CT images based on reconstructed super-resolution images and VGG neural network

Target Classification in Synthetic Aperture Radar Images Using Quantized Wavelet Scattering Networks

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