SAR Automatic Target Recognition Based on Multiview Deep Learning Framework

Pei, Jifang; Huang, Yulin; Huo, Weibo; Zhang, Yin; Yang, Jianyu; Yeo, Tat‐Soon

doi:10.1109/tgrs.2017.2776357

Cited by 268 publications

(149 citation statements)

References 37 publications

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“…Traditional SAR target recognition methods mainly concentrate strategy in the designed CNN to denoise the SAR target images by subtracting the recovered speckle component from the noise component. Pei et al [26] augmented the data by generating sufficient multi-view SAR data and fed the expanded SAR data into a designed CNN with a multi-input parallel network topology for identification. Dong et al [27] utilized spatial polarization information and XGBoost to perform classification experiments on the PolSAR images of the Gaofen-3 satellite, and proved that the combination of spatial information helps improve the overall performance.…”

Section: Introductionmentioning

confidence: 99%

TAI-SARNET: Deep Transferred Atrous-Inception CNN for Small Samples SAR ATR

Ying

Chen

Zhai

et al. 2020

Sensors

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Since Synthetic Aperture Radar (SAR) targets are full of coherent speckle noise, the traditional deep learning models are difficult to effectively extract key features of the targets and share high computational complexity. To solve the problem, an effective lightweight Convolutional Neural Network (CNN) model incorporating transfer learning is proposed for better handling SAR targets recognition tasks. In this work, firstly we propose the Atrous-Inception module, which combines both atrous convolution and inception module to obtain rich global receptive fields, while strictly controlling the parameter amount and realizing lightweight network architecture. Secondly, the transfer learning strategy is used to effectively transfer the prior knowledge of the optical, non-optical, hybrid optical and non-optical domains to the SAR target recognition tasks, thereby improving the model's recognition performance on small sample SAR target datasets. Finally, the model constructed in this paper is verified to be 97.97% on ten types of MSTAR datasets under standard operating conditions, reaching a mainstream target recognition rate. Meanwhile, the method presented in this paper shows strong robustness and generalization performance on a small number of randomly sampled SAR target datasets.

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Section: Introductionmentioning

confidence: 99%

TAI-SARNET: Deep Transferred Atrous-Inception CNN for Small Samples SAR ATR

Ying

Chen

Zhai

et al. 2020

Sensors

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“…As one of the hottest topics related to SAR remote-sensing applications, SAR automatic target recognition (ATR) focuses on the recognition of the interest targets from 2dimensional (2D) high-resolution SAR images. SAR ATR algorithms can be roughly categorized into template-based methods and model-based methods [2,3]. With respect to template-based methods, model-based ones can achieve better performance.…”

Section: Introductionmentioning

confidence: 99%

“…As for the classifier design, some advanced classifiers such as support vector machine (SVM) [8], sparse representation [7,[9][10][11], and convolutional neural networks (CNN) [1,5,8] have been employed. Algorithms based on CNN [1,5,8] or other deep learning [3] have been enriched enormously. However, due to the special complexity of SAR images and shortage of data amount, these algorithms usually suffer from overfitting and local minima [2].…”

Section: Introductionmentioning

confidence: 99%

Product Dictionary Learning-Based SAR Target Configuration Recognition

Liu

Chen

et al. 2020

International Journal of Antennas and Propagation

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Dictionary construction is a key factor for the sparse representation-(SR-) based algorithms. It has been verified that the learned dictionaries are more effective than the predefined ones. In this paper, we propose a product dictionary learning (PDL) algorithm to achieve synthetic aperture radar (SAR) target configuration recognition. e proposed algorithm obtains the dictionaries from a statistical standpoint to enhance the robustness of the proposed algorithm to noise. And, taking the inevitable multiplicative speckle in SAR images into account, the proposed algorithm employs the product model to describe SAR images. A more accurate description of the SAR image results in higher recognition rates. e accuracy and robustness of the proposed algorithm are validated by the moving and stationary target acquisition and recognition (MSTAR) database.

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“…They obtained the matched precision compared with the method in [18] under the premise of reducing 30% training data. Multiview SAR data was generated and a multi-input CNN architecture was proposed in [20] to get the features of targets from different views. In order to reduce the demand of extensive labeled samples, high-order features extracted by CNN were made into feature dictionary, and the end-to-end training was carried out through feature metric and two-stage optimization [21].…”

mentioning

confidence: 99%

Automatic Design of CNNs via Differentiable Neural Architecture Search for PolSAR Image Classification

Dong

Zou

Zhang

et al. 2020

IEEE Trans. Geosci. Remote Sensing

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Convolutional neural networks (CNNs) have shown good performance in polarimetric synthetic aperture radar (PolSAR) image classification due to the automation of feature engineering. Excellent hand-crafted architectures of CNNs incorporated the wisdom of human experts, which is an important reason for CNN's success. However, the design of the architectures is a difficult problem, which needs a lot of professional knowledge as well as computational resources. Moreover, the architecture designed by hand might be suboptimal, because it is only one of thousands of unobserved but objective existed paths. Considering that the success of deep learning is largely due to its automation of the feature engineering process, how to design automatic architecture searching methods to replace the handcrafted ones is an interesting topic. In this paper, we explore the application of neural architecture search (NAS) in PolSAR area for the first time. Different from the utilization of existing NAS methods, we propose a differentiable architecture search (DAS) method which is customized for PolSAR classification. The proposed DAS is equipped with a PolSAR tailored search space and an improved one-shot search strategy. By DAS, the weights parameters and architecture parameters (corresponds to the hyperparameters but not the topologies) can be optimized by stochastic gradient descent method during the training. The optimized architecture parameters should be transformed into corresponding CNN architecture and re-train to achieve highprecision PolSAR classification. In addition, complex-valued DAS is developed to take into account the characteristics of PolSAR images so as to further improve the performance. Experiments on three PolSAR benchmark datasets show that the CNNs obtained by searching have better classification performance than the hand-crafted ones. He is currently a Professor and Vice Head with the Department of Information Engineering, Harbin Institute of Technology. His research interests include SAR image processing, polarimetric SAR, and polarimetric SAR interferometry.

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SAR Automatic Target Recognition Based on Multiview Deep Learning Framework

Cited by 268 publications

References 37 publications

TAI-SARNET: Deep Transferred Atrous-Inception CNN for Small Samples SAR ATR

TAI-SARNET: Deep Transferred Atrous-Inception CNN for Small Samples SAR ATR

Product Dictionary Learning-Based SAR Target Configuration Recognition

Automatic Design of CNNs via Differentiable Neural Architecture Search for PolSAR Image Classification

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