SAR image target recognition via deep Bayesian generative network

Guo, Dandan; Chen, Bin

doi:10.1109/rsip.2017.7958814

Cited by 4 publications

(3 citation statements)

References 5 publications

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“…Some weak classifiers were trained by several subsets of SAR image patches to generate the projection vectors, which were then input into DBN to learn discriminative features for classification. An unsupervised deep generative networkpoisson gamma belief network (PGBN) was proposed to extract multi-layer feature from SAR images data for targets classification tasks in [352]. An unsupervised PolSAR image classification method using deep embedding network-SAEs was built in [353], which used SVD method to obtain lowdimensional manifold features as the inputs of SAEs, and the clustering algorithm determined the final unsupervised classification results.…”

Section: A Sar Images Processingmentioning

confidence: 99%

A Comprehensive Survey of Machine Learning Applied to Radar Signal Processing

Lang,

Fu,

Martorella

et al. 2020

Preprint

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Modern radar systems have high requirements in terms of accuracy, robustness and real-time capability when operating on increasingly complex electromagnetic environments. Traditional radar signal processing (RSP) methods have shown some limitations when meeting such requirements, particularly in matters of target classification. With the rapid development of machine learning (ML), especially deep learning, radar researchers have started integrating these new methods when solving RSP-related problems. This paper aims at helping researchers and practitioners to better understand the application of ML techniques to RSP-related problems by providing a comprehensive, structured and reasoned literature overview of ML-based RSP techniques. This work is amply introduced by providing general elements of ML-based RSP and by stating the motivations behind them. The main applications of ML-based RSP are then analysed and structured based on the application field. This paper then concludes with a series of open questions and proposed research directions, in order to indicate current gaps and potential future solutions and trends.

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Section: A Sar Images Processingmentioning

confidence: 99%

A Comprehensive Survey of Machine Learning Applied to Radar Signal Processing

Lang,

Fu,

Martorella

et al. 2020

Preprint

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“…This method improves the utilization rate of underwater target information and significantly improves the accuracy of target recognition, as shown in Figure 2. To extract multilayer features from sonar image, Guo and Chen 21 proposed the naïve Bayes Poisson gamma belief network (PGBN) model based on PGBN and Bayes’ theorem, which improve the training efficiency of the model. Moreover, the recognition accuracy can reach 93.85%, which is better than PGBN and other models, such as three-layer restricted Boltzmann machine, similarity deep belief network (DBN), DBN, SVM, and kernel SVM.…”

Section: Underwater Dangerous Target Recognition Technologymentioning

confidence: 99%

Underwater target recognition methods based on the framework of deep learning: A survey

Teng

Zhao

2020

International Journal of Advanced Robotic Systems

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The accuracy of underwater target recognition by autonomous underwater vehicle (AUV) is a powerful guarantee for underwater detection, rescue, and security. Recently, deep learning has made significant improvements in digital image processing for target recognition and classification, which makes the underwater target recognition study becoming a hot research field. This article systematically describes the application of deep learning in underwater image analysis in the past few years and briefly expounds the basic principles of various underwater target recognition methods. Meanwhile, the applicable conditions, pros and cons of various methods are pointed out. The technical problems of AUV underwater dangerous target recognition methods are analyzed, and corresponding solutions are given. At the same time, we prospect the future development trend of AUV underwater target recognition.

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“…Machine learning methods have attracted increasing attention because appropriate models can be formed while using these methods. Machine learning methods commonly used for image recognition include support vector machines (SVM), AdaBoost, and Bayesian neural network [16][17][18][19][20][21][22][23][24][25]. In order to obtain better recognition results, traditional machine learning methods require preprocessing images, such as denoising and feature extraction.…”

Section: Introductionmentioning

confidence: 99%

A Deep Convolutional Generative Adversarial Networks (DCGANs)-Based Semi-Supervised Method for Object Recognition in Synthetic Aperture Radar (SAR) Images

et al. 2018

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Synthetic aperture radar automatic target recognition (SAR-ATR) has made great progress in recent years. Most of the established recognition methods are supervised, which have strong dependence on image labels. However, obtaining the labels of radar images is expensive and time-consuming. In this paper, we present a semi-supervised learning method that is based on the standard deep convolutional generative adversarial networks (DCGANs). We double the discriminator that is used in DCGANs and utilize the two discriminators for joint training. In this process, we introduce a noisy data learning theory to reduce the negative impact of the incorrectly labeled samples on the performance of the networks. We replace the last layer of the classic discriminators with the standard softmax function to output a vector of class probabilities so that we can recognize multiple objects. We subsequently modify the loss function in order to adapt to the revised network structure. In our model, the two discriminators share the same generator, and we take the average value of them when computing the loss function of the generator, which can improve the training stability of DCGANs to some extent. We also utilize images of higher quality from the generated images for training in order to improve the performance of the networks. Our method has achieved state-of-the-art results on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset, and we have proved that using the generated images to train the networks can improve the recognition accuracy with a small number of labeled samples.

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SAR image target recognition via deep Bayesian generative network

Cited by 4 publications

References 5 publications

A Comprehensive Survey of Machine Learning Applied to Radar Signal Processing

A Comprehensive Survey of Machine Learning Applied to Radar Signal Processing

Underwater target recognition methods based on the framework of deep learning: A survey

A Deep Convolutional Generative Adversarial Networks (DCGANs)-Based Semi-Supervised Method for Object Recognition in Synthetic Aperture Radar (SAR) Images

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