Target Recognition in Synthetic Aperture Radar Images via Matching of Attributed Scattering Centers

Ding, Baiyuan; Wen, Gongjian; Huang, Xiaohong; Ma, Conghui; Yang, Xiu

doi:10.1109/jstars.2017.2671919

Cited by 141 publications

(110 citation statements)

References 29 publications

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“…In general, the traditional hand-designed features include two categories: the generalized features [1][2][3] and the SAR-specialized features [4][5][6][7]. The former ones involve features from other domains considering little of the characteristics of SAR imagery, while the latter ones refer to those designed for specific SAR ATR tasks.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Scale U-Shaped Convolution Auto-Encoder Based on Pyramid Pooling Module for Object Recognition in Synthetic Aperture Radar Images

Tian

Lin

Gao

et al. 2020

Sensors

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Although unsupervised representation learning (RL) can tackle the performance deterioration caused by limited labeled data in synthetic aperture radar (SAR) object classification, the neglected discriminative detailed information and the ignored distinctive characteristics of SAR images can lead to performance degradation. In this paper, an unsupervised multi-scale convolution auto-encoder (MSCAE) was proposed which can simultaneously obtain the global features and local characteristics of targets with its U-shaped architecture and pyramid pooling modules (PPMs). The compact depth-wise separable convolution and the deconvolution counterpart were devised to decrease the trainable parameters. The PPM and the multi-scale feature learning scheme were designed to learn multi-scale features. Prior knowledge of SAR speckle was also embedded in the model. The reconstruction loss of the MSCAE was measured by the structural similarity index metric (SSIM) of the reconstructed data and the images filtered by the improved Lee sigma filter. A speckle suppression restriction was also added in the objective function to guarantee that the speckle suppression procedure would take place in the feature learning stage. Experimental results with the MSTAR dataset under the standard operating condition and several extended operating conditions demonstrated the effectiveness of the proposed model in SAR object classification tasks.

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Section: Introductionmentioning

confidence: 99%

A Multi-Scale U-Shaped Convolution Auto-Encoder Based on Pyramid Pooling Module for Object Recognition in Synthetic Aperture Radar Images

Tian

Lin

Gao

et al. 2020

Sensors

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“…Generally, scattering center models includes the damped exponential (DE) model, the geometrical theory of diffraction (GTD) model and the attributed scattering center model . The DE model has a high range resolution, but it cannot accurately describe the edge diffraction scattering for relative wide bandwidths.…”

Section: Introductionmentioning

confidence: 99%

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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“…Principal component analysis (PCA) and linear discriminant analysis (LDA) [2] are usually used for feature extraction from SAR images. Other features, such as geometrical descriptors [3], attributed scattering centers [4,5], and monogenic spectrums [6], are also applied to SAR target recognition. As for the decision engines, various classifiers, including support vector machines (SVM) [7], sparse representation-based classification (SRC) [8,9], and convolutional neural networks (CNN) [10] are employed for target recognition and have achieved delectable results.…”

Section: Introductionmentioning

confidence: 99%

Exploiting Multi-View SAR Images for Robust Target Recognition

Ding

Wen

2017

Remote Sensing

Self Cite

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Abstract:The exploitation of multi-view synthetic aperture radar (SAR) images can effectively improve the performance of target recognition. However, due to the various extended operating conditions (EOCs) in practical applications, some of the collected views may not be discriminative enough for target recognition. Therefore, each of the input views should be examined before being passed through to multi-view recognition. This paper proposes a novel structure for multi-view SAR target recognition. The multi-view images are first classified by sparse representation-based classification (SRC). Based on the output residuals, a reliability level is calculated to evaluate the effectiveness of a certain view for multi-view recognition. Meanwhile, the support samples for each view selected by SRC collaborate to construct an enhanced local dictionary. Then, the selected views are classified by joint sparse representation (JSR) based on the enhanced local dictionary for target recognition. The proposed method can eliminate invalid views for target recognition while enhancing the representation capability of JSR. Therefore, the individual discriminability of each valid view as well as the inner correlation among all of the selected views can be exploited for robust target recognition. Experiments are conducted on the moving and stationary target acquisition recognition (MSTAR) dataset to demonstrate the validity of the proposed method.

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Target Recognition in Synthetic Aperture Radar Images via Matching of Attributed Scattering Centers

Cited by 141 publications

References 29 publications

A Multi-Scale U-Shaped Convolution Auto-Encoder Based on Pyramid Pooling Module for Object Recognition in Synthetic Aperture Radar Images

A Multi-Scale U-Shaped Convolution Auto-Encoder Based on Pyramid Pooling Module for Object Recognition in Synthetic Aperture Radar Images

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

Exploiting Multi-View SAR Images for Robust Target Recognition

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