Synthetic aperture radar automatic target recognition with three strategies of learning and representation

Zhao, Qun

doi:10.1117/1.602495

Cited by 53 publications

(42 citation statements)

References 36 publications

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“…[1][2][3] For example, Defense Advanced Research Projects Agency (DARPA) obtained SAR images of ground targets in different poses, which contain the moving and stationary target acquisition and recognition (MSTAR) public release data set, 4 which many researchers use. [5][6][7][8][9][10][11][12][13][14][15] In the majority of cases, they apply a double-stage approach for object recognition.…”

Section: Introductionmentioning

confidence: 99%

Support subspaces method for synthetic aperture radar automatic target recognition

Fursov¹,

Zherdev²,

Kazanskiy

2016

International Journal of Advanced Robotic Systems

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This article offers a new object recognition approach that gives high quality using synthetic aperture radar images. The approach includes image preprocessing, clustering and recognition stages. At the image preprocessing stage, we compute the mass centre of object images for better image matching. A conjugation index of a recognition vector is used as a distance function at clustering and recognition stages. We suggest a construction of the so-called support subspaces, which provide high recognition quality with a significant dimension reduction. The results of the experiments demonstrate that the proposed method provides higher recognition quality (97.8%) than such methods as support vector machine (95.9%), deep learning based on multilayer auto-encoder (96.6%) and adaptive boosting (96.1%). The proposed method is stable for objects processed from different angles.

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Section: Introductionmentioning

confidence: 99%

Support subspaces method for synthetic aperture radar automatic target recognition

Fursov¹,

Zherdev²,

Kazanskiy

2016

International Journal of Advanced Robotic Systems

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“…The base line for the comparison is the template matching method [36]. For our approach, it can be seen that the misclassification error is 2.1% from Table 2.…”

Section: -Class Problemmentioning

confidence: 96%

Sar Target Classification Using Bayesian Compressive Sensing With Scattering Centers Features

Zhang¹,

Qin²,

Li³

2013

PIER

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Abstract-The emerging field of compressed sensing provides sparse reconstruction, which has demonstrated promising results in the areas of signal processing and pattern recognition. In this paper, a new approach for synthetic aperture radar (SAR) target classification is proposed based on Bayesian compressive sensing (BCS) with scattering centers features. Scattering centers features is extracted as a l 1 -norm sparse problem on the basis of SAR observation physical model, which can improve discrimination ability compared with original SAR image. Using an overcomplete dictionary constructed by training samples, BCS is utilized to design targets classifier. For target classification performance evaluation, the proposed method is compared with several state-of-art methods through experiments on Moving and Stationary Target Acquisition and Recognition (MSTAR) public release database. Experiment results illustrate the effectiveness and robustness of the proposed approach.

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“…Combining the goal of small reconstruction error with that of sparseness, the following objective function to be minimized can be arrived [5].…”

Section: Non-negative Sparse Codingmentioning

confidence: 99%

“…SVM classifiers are based on the principle of structural risk minimization [5]. Assuming a set of N training samples and labels {v i , y i } i=1,...,N , the result of training the SVM is the hyperplane decision function…”

Section: Svmmentioning

confidence: 99%

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HRR Profiles Time-Frequency Non-Negative Sparse Coding for Sar Target Classification

Zhang¹,

Wu²,

Liu³

et al. 2014

PIER B

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Abstract-A new approach to classify synthetic aperture radar (SAR) targets is presented based on high range resolution (HRR) profiles time-frequency matrix non-negative sparse coding (NNSC). Firstly, SAR target images have been converted into HRR profiles. And the non-negative time-frequency matrix for each of the profiles is obtained by using an adaptive Gaussian representation (AGR). Secondly, NNSC is applied to learn target time-frequency basis of the training set. Feature vectors are constructed by projecting each HRR profile time-frequency matrix to low dimensional time-frequency basis space. Finally, the target classification decision is found with support vector machine and nearest neighbor algorithm respectively. To demonstrate the performance of the proposed approach, experiments are performed with Moving and Stationary Target Acquisition and Recognition (MSTAR) public release SAR database. The experimental results support the effectiveness of the proposed technique for SAR target classification.

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Synthetic aperture radar automatic target recognition with three strategies of learning and representation

Cited by 53 publications

References 36 publications

Support subspaces method for synthetic aperture radar automatic target recognition

Support subspaces method for synthetic aperture radar automatic target recognition

Sar Target Classification Using Bayesian Compressive Sensing With Scattering Centers Features

HRR Profiles Time-Frequency Non-Negative Sparse Coding for Sar Target Classification

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