Fusing Deep Learning and Sparse Coding for SAR ATR

Kechagias‐Stamatis, Odysseas; Aouf, Nabil

doi:10.1109/taes.2018.2864809

Cited by 71 publications

(44 citation statements)

References 58 publications

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“…Szegedy et al [9] introduced the inception model, which mainly generated diverse visual features by combining various sizes of convolution kernels. Kechagias-Stamatis et al [10] proposed a new structure combining convolutional neural network with sparse coding to achieve the highest performance in the field of automatic target recognition.…”

Section: Introductionmentioning

confidence: 99%

A New Improved Learning Algorithm for Convolutional Neural Networks

Yang

Zhao

et al. 2020

Processes

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The back-propagation (BP) algorithm is usually used to train convolutional neural networks (CNNs) and has made greater progress in image classification. It updates weights with the gradient descent, and the farther the sample is from the target, the greater the contribution of it to the weight change. However, the influence of samples classified correctly but that are close to the classification boundary is diminished. This paper defines the classification confidence as the degree to which a sample belongs to its correct category, and divides samples of each category into dangerous and safe according to a dynamic classification confidence threshold. Then a new learning algorithm is presented to penalize the loss function with danger samples but not all samples to enable CNN to pay more attention to danger samples and to learn effective information more accurately. The experiment results, carried out on the MNIST dataset and three sub-datasets of CIFAR-10, showed that for the MNIST dataset, the accuracy of Non-improve CNN reached 99.246%, while that of PCNN reached 99.3%; for three sub-datasets of CIFAR-10, the accuracies of Non-improve CNN are 96.15%, 88.93%, and 94.92%, respectively, while those of PCNN are 96.44%, 89.37%, and 95.22%, respectively.

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Section: Introductionmentioning

confidence: 99%

A New Improved Learning Algorithm for Convolutional Neural Networks

Yang

Zhao

et al. 2020

Processes

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“…At present, the target recognition and classification methods of remote sensing images can be divided into traditional statistical methods [1][2][3][4][5][6][7][8], machine learning methods [9][10][11], and deep learning algorithms [12][13][14]. The traditional statistical methods realize the classification by using statistical analysis of the gray value and texture of image.…”

Section: Introductionmentioning

confidence: 99%

Extraction Method of Offshore Mariculture Area under Weak Signal based on Multisource Feature Fusion

Liu

Jiang

Zhang

et al. 2020

JMSE

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Mariculture is crucial in environmental monitoring and safety assurance of marine environments. Certain mariculture areas are often partially or completely submerged in water, which causes the target signal to be extremely weak and difficult to detect. A method of target recognition and classification based on the convolutional neural network called semantic segmentation can fully consider the space spectrum and context semantic information. Therefore, this study proposes a target extraction method on the basis of multisource feature fusion, such as nNDWI and G/R ratio. In this work, the proposed recognition algorithm is verified under the conditions of uniform distribution of strong, weak, and extremely weak signals. Results show that the G/R feature is superior under the condition of uniform distribution of strong and weak signals. Its mean pixels accuracy is 2.32% higher than RGB (combination of red band, green band, and blue band), and its overall classification accuracy is 98.84%. Under the condition of extremely weak signal, the MPA of the multisource feature method based on the combination of G/R and nNDWI is 10.76% higher than RGB, and the overall classification accuracy is 82.02%. Under this condition, the G/R features highlight the target, and nNDWI suppresses noise. The proposed method can effectively extract the information of weak signal in the marine culture area and provide technical support for marine environmental monitoring and marine safety assurance.

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“…Shang et al added an information recorder to CNN to remember and store the spatial features of the samples, and then used spatial similarity information of the recorded features to predict the unknown sample labels [19]. Kechagias-Stamatis et al fused a convolutional neural network module with a sparse coding module under a decision level scheme, which can adaptively alter the fusion weights that are based on the SAR images [25]. Pei et al proposed a multiple-view DCNN (m-VDCNN) to extract the features from target images with different azimuth angles [26].…”

Section: Introductionmentioning

confidence: 99%

SAR ATR of Ground Vehicles Based on ESENet

2019

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In recent studies, synthetic aperture radar (SAR) automatic target recognition (ATR) algorithms that are based on the convolutional neural network (CNN) have achieved high recognition rates in the moving and stationary target acquisition and recognition (MSTAR) dataset. However, in a SAR ATR task, the feature maps with little information automatically learned by CNN will disturb the classifier. We design a new enhanced squeeze and excitation (enhanced-SE) module to solve this problem, and then propose a new SAR ATR network, i.e., the enhanced squeeze and excitation network (ESENet). When compared to the available CNN structures that are designed for SAR ATR, the ESENet can extract more effective features from SAR images and obtain better generalization performance. In the MSTAR dataset containing pure targets, the proposed method achieves a recognition rate of 97.32% and it exceeds the available CNN-based SAR ATR algorithms. Additionally, it has shown robustness to large depression angle variation, configuration variants, and version variants.

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Fusing Deep Learning and Sparse Coding for SAR ATR

Cited by 71 publications

References 58 publications

A New Improved Learning Algorithm for Convolutional Neural Networks

A New Improved Learning Algorithm for Convolutional Neural Networks

Extraction Method of Offshore Mariculture Area under Weak Signal based on Multisource Feature Fusion

SAR ATR of Ground Vehicles Based on ESENet

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