SAR Target Recognition via Local Sparse Representation of Multi-Manifold Regularized Low-Rank Approximation

Yu, Meiting; Dong, Ganggang; Fan, Houbao; Kuang, Gangyao

doi:10.3390/rs10020211

Cited by 54 publications

(10 citation statements)

References 39 publications

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“…Next, the linear structures recognition algorithm is proposed to determine which two endpoints can be formed into a linear structure [31][32][33]. Let Ω l = {E, F, G, H} be the set of endpoints that belongs to linear structure EF and linear structure GH.…”

Section: Linear Structures Recognitionmentioning

confidence: 99%

Parametric Synthetic Aperture Radar Image Recovery for Multiple Linear Structures: An Image Domain Approach

et al. 2020

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The linear structures in synthetic aperture radar (SAR) images can provide important geometric information regarding the illuminated objects. However, the linear structures of various objects sometimes disappear in traditional SAR images, which severely affects the application in automated scene analysis and information extraction techniques. This paper proposes a parametric SAR image recovery method for linear structures of extended targets. By extracting the spatial phase distribution feature in image domain, the proposed method is used to identify the endpoints among all of the scattered points in SAR images and reconstruct the disappeared linear structures based on the scattering model. The method can be generally divided into three procedures: endpoints and point targets classification, linear structures recognition, and linear structures reconstruction. In the first step, the endpoints of linear structures and point targets are classified by setting a threshold related to the spatial phase distribution feature. Afterwards, the linear structures recognition method is used to determine which two endpoints can be formed into a linear structure. Finally, the parametric scattering models are used to reconstruct the disappeared linear structures. Experiments are conducted on both computer simulations and the data that were acquired by microwave anechoic chamber experiment, tower crane radar experiment, and unmanned vehicle radar experiment in order to validate the effectiveness and robustness of the proposed method.

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Section: Linear Structures Recognitionmentioning

confidence: 99%

Parametric Synthetic Aperture Radar Image Recovery for Multiple Linear Structures: An Image Domain Approach

et al. 2020

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“…Due to the physical differences and structural modifications, a certain target may have had several different configurations [12]. The template set could only contain a special configuration, but the test images could have different configurations.…”

Section: Configuration Variancementioning

confidence: 99%

“…NMF has been employed for target recognition of SAR images in [8,9]. Some manifold learning methods have also been used for SAR feature extraction, with good ATR performances [10][11][12]. The last one is the scattering center features.…”

Section: Introductionmentioning

confidence: 99%

Target Reconstruction Based on Attributed Scattering Centers with Application to Robust SAR ATR

Fan

Tomas

2018

Remote Sensing

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This paper proposes a synthetic aperture radar (SAR) automatic target recognition (ATR) method by target reconstruction based on attributed scattering centers (ASCs). The extracted ASCs can effectively describe the electromagnetic scattering characteristics of the target, while eliminating the background clutters and noises. Therefore, the ASCs are discriminative features for SAR ATR. The neighbor matching algorithm was used to build the correspondence between the test ASC set and corresponding template ASC set. Afterwards, the selected template ASCs were used to reconstruct the template image, whereas all the test ASCs were used to reconstruct the test image based on the ASC model. A similarity measure was further designed based on the reconstructed images for target recognition. Compared with traditional ASC matching methods, the complex one-to-one correspondence between two ASC sets was avoided. Moreover, all the attributes of the ASCs were utilized during the target reconstruction. Therefore, the proposed method can better exploit the discriminability of ASCs to improve the ATR performance. To evaluate the effectiveness and robustness of the proposed method, extensive experiments on the moving and stationary target acquisition and recognition (MSTAR) dataset were conducted under both the standard operating condition (SOC) and typical extended operating conditions (EOCs).

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“…10 and 11, respectively. Some other projection features developed from manifold learning were also used for SAR ATR 12 – 14 such as non-negative matrix factorization 12 and neighborhood geometric center scaling embedding 13 . Signal processing algorithms like wavelet analysis 15 , 16 and monogenic signal 17 , 18 could transform the original SAR images to other domains thus finding the robust features.…”

Section: Introductionmentioning

confidence: 99%

Multiview synthetic aperture radar target recognition method using joint sparse representation and random weight matrix

Zeng

Gao

Yang

et al. 2023

J. Appl. Rem. Sens.

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.Multiview synthetic aperture radar (SAR) images could provide richer information for target recognition as reported in previous works. This study exploits multiview SAR images with application to target recognition via the combination of joint sparse representation (JSR) and random weight matrix. First, the multiview SAR images are clustered into several view sets via a coarse clustering algorithm. In this way, the views in each set tend to share high correlations so they can be properly represented with JSR. For the reconstruction errors from different views, they are fused through a random weight matrix, which includes a rich set of linear weight vectors. Afterwards, the fused errors at different choices of weight vectors are analyzed to form a decision value for target recognition, which could better reflect the differences between different classes through multiview SAR images in comparison with traditional JSR-based methods. Therefore, the overall SAR target recognition performance can be effectively enhanced. Experiments are investigated on the moving and stationary target acquisition and recognition dataset under several operating conditions. The results reveal the high effectiveness of the proposed method under standard operating condition and good robustness under typical extended operating conditions including depression angle variance, configuration variants, noise corruption, partial occlusion, and resolution variance.

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SAR Target Recognition via Local Sparse Representation of Multi-Manifold Regularized Low-Rank Approximation

Cited by 54 publications

References 39 publications

Parametric Synthetic Aperture Radar Image Recovery for Multiple Linear Structures: An Image Domain Approach

Parametric Synthetic Aperture Radar Image Recovery for Multiple Linear Structures: An Image Domain Approach

Target Reconstruction Based on Attributed Scattering Centers with Application to Robust SAR ATR

Multiview synthetic aperture radar target recognition method using joint sparse representation and random weight matrix

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