MFFA-SARNET: Deep Transferred Multi-Level Feature Fusion Attention Network with Dual Optimized Loss for Small-Sample SAR ATR

Zhai, Yongjie; Deng, Wenbo; Lan, Tian; Sun, Bing; Ying, Zilu; Gan, Junying; Mai, Chaoyun; Li, Jingwen; Labati, Ruggero Donida; Piuri, Vincenzo; Scotti, Fabio

doi:10.3390/rs12091385

Cited by 17 publications

(7 citation statements)

References 47 publications

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“…High-resolution radar images in range and azimuth can be obtained by Synthetic Aperture Radar (SAR), which includes synthetic aperture principle, pulse compression technology, and signal processing technology. Compared with optical and infrared sensors, SAR has the advantages of day-andnight, all-weather, and the ability to penetrate obstacles such as clouds and vegetation [1][2][3][4][5][6]. With the increasing SAR imaging resolution, SAR has been diversely utilized in military and civilian fields, such as marine, land monitoring [7], and weapon guidance [8].…”

Section: Introductionmentioning

confidence: 99%

Few Samples of SAR Automatic Target Recognition Based on Enhanced-Shape CNN

Huang

Liu

Meng

2021

Journal of Mathematics

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Synthetic Aperture Radar (SAR), as one of the important and significant methods for obtaining target characteristics in the field of remote sensing, has been applied to many fields including intelligence search, topographic surveying, mapping, and geological survey. In SAR field, the SAR automatic target recognition (SAR ATR) is a significant issue. However, on the other hand, it also has high application value. The development of deep learning has enabled it to be applied to SAR ATR. Some researchers point out that existing convolutional neural network (CNN) paid more attention to texture information, which is often not as good as shape information. Wherefore, this study designs the enhanced-shape CNN, which enhances the target shape at the input. Further, it uses an improved attention module, so that the network can highlight target shape in SAR images. Aiming at the problem of the small scale of the existing SAR data set, a small sample experiment is conducted. Enhanced-shape CNN achieved a recognition rate of 99.29% when trained on the full training set, while it is 89.93% on the one-eighth training data set.

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Section: Introductionmentioning

confidence: 99%

Few Samples of SAR Automatic Target Recognition Based on Enhanced-Shape CNN

Huang

Liu

Meng

2021

Journal of Mathematics

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“…In deep convolutional neural networks, low-level image features have high resolution and more detailed information, but low-level image features are more noisy and have poor semantics; while high-level image features have higher-level semantics, but its resolution is lower, the detailed information are less, and the high-level features are more prone to over-fitting; the middle-level image features are in-between the high-level and low-level features [ 20 , 21 ]. Effective fusion of these three features can improve the classification performance and make the model more robust.…”

Section: Methodsmentioning

confidence: 99%

“…At present, single-apect SAR target recognition under the small number of training samples has been studied [ 19 , 20 , 21 ]. However, there are relatively few studies on multi-apect SAR target recognition with a small number of training samples.…”

Section: Introductionmentioning

confidence: 99%

Multi-Aspect SAR Target Recognition Based on Prototypical Network with a Small Number of Training Samples

Zhao

Huang

Xin

et al. 2021

Sensors

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At present, synthetic aperture radar (SAR) automatic target recognition (ATR) has been deeply researched and widely used in military and civilian fields. SAR images are very sensitive to the azimuth aspect of the imaging geomety; the same target at different aspects differs greatly. Thus, the multi-aspect SAR image sequence contains more information for classification and recognition, which requires the reliable and robust multi-aspect target recognition method. Nowadays, SAR target recognition methods are mostly based on deep learning. However, the SAR dataset is usually expensive to obtain, especially for a certain target. It is difficult to obtain enough samples for deep learning model training. This paper proposes a multi-aspect SAR target recognition method based on a prototypical network. Furthermore, methods such as multi-task learning and multi-level feature fusion are also introduced to enhance the recognition accuracy under the case of a small number of training samples. The experiments by using the MSTAR dataset have proven that the recognition accuracy of our method can be close to the accruacy level by all samples and our method can be applied to other feather extraction models to deal with small sample learning problems.

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“…The mainstream neural networks mai- ntain high recognition rates for recognizing SAR targets without noise interference, however, the recognition performance plummeted after the addition of a little noise to the testing set. When the parameter a is set to 1, the recognition rates of Res-Net18, RestNet50, SIN-CNN [12], EfficieNetV2, Wavelet-SRNet [14], RestNet18 with DWT (Discrete Wavelet Transform), MFFA-SARNet [55], A_ConvNet [1], and MFCNN [13] drop sharply from 96.19%, 94.88%, 98.81%, 99.30%, 97.5%, 97.81%, 95.78%, 95.21% and 96.88% to 74.65%, 61.47%, 36.30%, 57.5%, 50.33%, 56.28%, 53.96%, 63.59% and 52.15%, respectively. In the same condition, the recognition rates of our method only show a mild decrease from 98.21% to 93.60%.…”

Section: Recognition Performance Of Supervised Network Under Differen...mentioning

confidence: 99%

Dual Consistency Alignment Based Self-Supervised Learning for SAR Target Recognition With Speckle Noise Resistance

Zhai

Liao

Sun

et al. 2023

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

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Deep learning based on Convolutional Neural Networks (CNN) has been widely applied in Synthetic Aperture Radar (SAR) target recognition and made significant progress. However, due to the physical effects of the equipment used to collect images, various degrees of speckle noise will be introduced into SAR images. Traditional CNN based SAR target recognition methods are premised on the same noise intensity in the training and testing set, which is contrary to the target recognition in practice. To alleviate this problem, we propose a novel speckle noise resistant framework for SAR target recognition, called Dual Consistency Alignment based Self-Supervised Learning(DCA-SSL). Firstly, original SAR images are randomly added to speckle noise with different thresholds through multiplicative noise, after which contrastive pre-training is performed on unlabeled data. During this period, we combine instance pseudo-label consistency alignment and feature consistency alignment to align multiple threshold speckle noise views with original views under the same targets. Finally, the pre-trained model is migrated to the downstream SAR speckle noise target recognition task. In this article, speckle noise modeling is conducted based on MSTAR data testing set, and experiment results reveal that this method can adapt to different intensities of speckle noise, is robust to modeled SAR image recognition and maintains a high recognition rate even in small sample learning. Our code is available at: https://github.com/Jordan-Liao/DCA-SSL.

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MFFA-SARNET: Deep Transferred Multi-Level Feature Fusion Attention Network with Dual Optimized Loss for Small-Sample SAR ATR

Cited by 17 publications

References 47 publications

Few Samples of SAR Automatic Target Recognition Based on Enhanced-Shape CNN

Few Samples of SAR Automatic Target Recognition Based on Enhanced-Shape CNN

Multi-Aspect SAR Target Recognition Based on Prototypical Network with a Small Number of Training Samples

Dual Consistency Alignment Based Self-Supervised Learning for SAR Target Recognition With Speckle Noise Resistance

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