Robust method for the matching of attributed scattering centers with application to synthetic aperture radar automatic target recognition

Ding, Baiyuan; Wen, Gongjian; Zhong, Jing; Ma, Conghui; Yang, Xiu

doi:10.1117/1.jrs.10.016010

Cited by 49 publications

(46 citation statements)

References 19 publications

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“…The scattering center features reflect the electromagnetic scattering characteristics of the target such as attributed scattering centers (ASCs) [18,19]. ASCs describe the local structures of the target by several physically relevant parameters, which have been demonstrated notably effectively for SAR ATR especially under the extended operating conditions (EOCs) [19][20][21][22][23][24][25]. In [21], an ASC-matching method is proposed based on Bayesian theory with application to target recognition.…”

Section: Introductionmentioning

confidence: 99%

“…In [21], an ASC-matching method is proposed based on Bayesian theory with application to target recognition. Ding et al propose several ways to apply ASCs to SAR ATR, e.g., one-to-one ASC matching [22][23][24] and ASC-based target reconstruction [25]. Recently, the 3-D scattering center model-based SAR ATR methods have drawn the researchers' interests, where a 3-D scattering center model is established to describe the target's electromagnetic scatterings for feature prediction [26,27].…”

Section: Introductionmentioning

confidence: 99%

“…Specially, for the features without unified forms, e.g., the unordered scattering centers, a similarity or distance measure is often first defined for these features. Afterwards, the target type is determined based on the maximum similarity or minimum distance [19][20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

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Hierarchical Fusion of Convolutional Neural Networks and Attributed Scattering Centers with Application to Robust SAR ATR

Jiang

Zhou

2018

Remote Sensing

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This paper proposes a synthetic aperture radar (SAR) automatic target recognition (ATR) method via hierarchical fusion of two classification schemes, i.e., convolutional neural networks (CNN) and attributed scattering center (ASC) matching. CNN can work with notably high effectiveness under the standard operating condition (SOC). However, it can hardly cope with various extended operating conditions (EOCs), which are not covered by the training samples. In contrast, the ASC matching can handle many EOCs related to the local variations of the target by building a one-to-one correspondence between two ASC sets. Therefore, it is promising that both effectiveness and efficiency of the ATR method can be improved by combining the merits of the two classification schemes. The test sample is first classified by CNN. A reliability level calculated based on the outputs from CNN. Once there is a notably reliable decision, the whole recognition process terminates. Otherwise, the test sample will be further identified by ASC matching. To evaluate the performance of the proposed method, extensive experiments are conducted on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset under SOC and various EOCs. The results demonstrate the superior effectiveness and robustness of the proposed method compared with several state-of-the-art SAR ATR methods.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Hierarchical Fusion of Convolutional Neural Networks and Attributed Scattering Centers with Application to Robust SAR ATR

Jiang

Zhou

2018

Remote Sensing

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“…The scattering center features reflect the electromagnetic scattering characteristics of the target. Because of the rich physically relevant descriptions, the attributed scattering centers have been effectively used for SAR ATR [25][26][27][28][29]. However, most of these features aim to reduce the redundancy in the original SAR images and can hardly reduce the confusing information.…”

Section: Introductionmentioning

confidence: 99%

“…With the fast development of pattern recognition and machine learning techniques, many advanced classifiers [30][31][32][33][34][35][36][37] have been successfully applied to SAR ATR, such as the support vector machine (SVM) [28,29], sparse representation-based classification (SRC) [21,[31][32][33], convolutional neural network (CNN) [35], adaptive boosting (Adaboost) [36] and discriminative graphical models [37]. Among these classifiers, SRC is notably robust to EOCs such as noise corruption and partial occlusion [38].…”

Section: Introductionmentioning

confidence: 99%

Target Recognition in SAR Images Based on Information-Decoupled Representation

Chang

You

2018

Remote Sensing

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This paper proposes an automatic target recognition (ATR) method for synthetic aperture radar (SAR) images based on information-decoupled representation. A typical SAR image of a ground target can be divided into three parts: target region, shadow and background. From the aspect of SAR target recognition, the target region and shadow contain discriminative information. However, they also include some confusing information because of the similarities of different targets. The background mainly contains redundant information, which has little contribution to the target recognition. Because the target segmentation may impair the discriminative information in the target region, the relatively simpler shadow segmentation is performed to separate the shadow region for information decoupling. Then, the information-decoupled representations are generated, i.e., the target image, shadow and original image. The background is retained in the target image, which represents the coupling of target backscattering and background. The original image and generated target image are classified using the sparse representation-based classification (SRC). Then, their classification results are combined by a score-level fusion for target recognition. The shadow image is not used because of its lower discriminability and possible segmentation errors. To evaluate the performance of the proposed method, extensive experiments are conducted on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset under both standard operating condition (SOC) and various extended operating conditions (EOCs). The proposed method can correctly classify 10 classes of targets with the percentage of correct classification (PCC) of 94.88% under SOC. With the PCCs of 93.15% and 75.03% under configuration variance and 45 • depression angle, respectively, the superiority of the proposed is demonstrated in comparison with other methods. The robustness of the proposed method to both uniform and nonuniform shadow segmentation errors is validated with the PCCs over 93%. Moreover, with the maximum average precision of 0.9580, the proposed method is more effective than the reference methods on outlier rejection.

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Parameter estimation of the 2D‐GTD model and RCS reconstruction based on an improved 2D‐ESPRIT algorithm

Zheng

Zhang

Zong

et al. 2020

Int J RF Microw Comput Aided Eng

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The two‐dimensional estimating signal parameter via rotational invariance techniques (2D‐ESPRIT) algorithm is a classical method to estimate parameters of the two‐dimensional geometric theory of diffraction (2D‐GTD) model. While as signal‐to‐noise‐ratio (SNR) decreases, the parameter estimation performance of 2D‐ESPRIT algorithm is severely influenced. To solve this problem, a performance‐enhanced 2D‐ESPRIT algorithm is proposed in this article. The improved 2D‐ESPRIT algorithm combines the conjugate data with the original back‐scattered data and obtains a novel covariance matrix by squaring the original total covariance matrix. Simulation results indicate that the improved algorithm has a better noise robustness and a more stable parameter estimation performance than the classical ESPRIT algorithm and the classical TLS‐2D‐ESPRIT algorithm. To further validate the superiority of the improved 2D‐ESPRIT algorithm, reconstructed radar cross section (RCS) is presented in this article. Compared with the classical 2D‐ESPRIT algorithm, the proposed algorithm presents higher RCS fitting precision. Furthermore, the impacts of other factors on parameter estimation, such as matrix pencil parameters and paring parameters, are also studied in this article.

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Robust method for the matching of attributed scattering centers with application to synthetic aperture radar automatic target recognition

Cited by 49 publications

References 19 publications

Hierarchical Fusion of Convolutional Neural Networks and Attributed Scattering Centers with Application to Robust SAR ATR

Hierarchical Fusion of Convolutional Neural Networks and Attributed Scattering Centers with Application to Robust SAR ATR

Target Recognition in SAR Images Based on Information-Decoupled Representation

Parameter estimation of the 2D‐GTD model and RCS reconstruction based on an improved 2D‐ESPRIT algorithm

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