Multiset Canonical Correlations Analysis of Bidimensional Intrinsic Mode Functions for Automatic Target Recognition of SAR Images

Ding, Yong

doi:10.1155/2021/4392702

Cited by 5 publications

(4 citation statements)

References 55 publications

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“…Manifold learning algorithms using local embeddings were also validated in SAR target recognition with good performance [33][34][35]. Te signal processing algorithms including wavelet analysis [36,37], monogenic signal [38,39], and bidimensional empirical mode decomposition (BEMD) [40] were validated useful for SAR feature extraction. Dong et al decomposed SAR images based on the monogenic signal and classifed the multiscale features for SAR ATR [38,39].…”

Section: Introductionmentioning

confidence: 99%

“…Dong et al decomposed SAR images based on the monogenic signal and classifed the multiscale features for SAR ATR [38,39]. Chang et al developed a SAR ATR method based on BEMD [40]. Diferent from optical images, SAR images contain the electromagnetic characteristics of the targets such as scattering center [41][42][43][44][45][46][47][48] and polarizations [40,50].…”

Section: Introductionmentioning

confidence: 99%

“…Chang et al developed a SAR ATR method based on BEMD [40]. Diferent from optical images, SAR images contain the electromagnetic characteristics of the targets such as scattering center [41][42][43][44][45][46][47][48] and polarizations [40,50]. In [42][43][44][45], the attributed scattering centers were used for SAR target recognition based on diferent matching algorithms.…”

Section: Introductionmentioning

confidence: 99%

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Multitask Sparse Representation of Two-Dimensional Variational Mode Decomposition Components for SAR Target Recognition

Weng

2023

Scientific Programming

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A synthetic aperture radar (SAR) automatic target recognition (ATR) method is developed based on the two-dimensional variational mode decomposition (2D-VMD). 2D-VMD decomposes original SAR images into multiscale components, which depict the time-frequency properties of the targets. The original image and its 2D-VMD components are highly correlated, so the multitask sparse representation is chosen to jointly represent them. According to the resulted reconstruction errors of different classes, the target label of test sample can be classified. The moving and stationary target acquisition and recognition (MSTAR) dataset is used to set up the standard operating condition (SOC) and several extended operating conditions (EOCs) including configuration variants, depression angle variances, noise corruption, and partial occlusion to test and validate the proposed method. The results confirm the effectiveness and robustness of the proposed method compared with several state-of-the-art SAR ATR references.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multitask Sparse Representation of Two-Dimensional Variational Mode Decomposition Components for SAR Target Recognition

Weng

2023

Scientific Programming

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“…ATR technology has evolved from theoretical research to systematic development and application worldwide. Typical SAR ATR systems include semi-automatic IMINT processing (SAIP) and the moving and stationary target acquisition and recognition (MSTAR) program [7][8][9][10]. These systems describe target characteristics based on templates and models, respectively.…”

Section: Introductionmentioning

confidence: 99%

An SAR Image Automatic Target Recognition Method Based on the Scattering Parameter Gaussian Mixture Model

Qin,

Liu,

Ran

et al. 2023

Remote Sensing

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General synthetic aperture radar (SAR) image automatic target recognition (ATR) methods perform well under standard operation conditions (SOCs). However, they are not effective in extended operation conditions (EOCs). To improve the robustness of the ATR system under various EOCs, an ATR method for SAR images based on the scattering parameter Gaussian mixture model (GMM) is proposed in this paper. First, an improved active contour model (ACM) is used for target–background segmentation, which is more robust against noise than the constant false alarm rate (CFAR) method. Then, as the extracted attributed scattering center (ASC) is sensitive to noise and resolution, the GMM is constructed using the extracted ASC set. Next, the weighted Gaussian quadratic form distance (WGQFD) is adopted to measure the similarity of GMMs for the recognition task, thereby avoiding false alarms and missed alarms caused by the varying number of scattering centers. Moreover, adaptive aspect–frame division is employed to reduce the number of templates and improve recognition efficiency. Finally, based on the public measured MSTAR dataset, different EOCs are constructed under noise, resolution change, model change, depression angle change, and occlusion of different proportions. The experimental results under different EOCs demonstrate that the proposed method exhibits excellent robustness while maintaining low computation time.

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Robust target recognition in synthetic aperture radar images via correlation analysis and joint decision of multi-level bidimensional intrinsic mode functions by bidimensional variational mode decomposition

Zhang,

Wu,

Peng

2024

J. Electron. Imag.

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Synthetic aperture radar (SAR) images provide high-resolution observations for military targets that can be used for target recognition. A SAR target recognition method is proposed based on correlation analysis and joint decision of multi-level bidimensional intrinsic mode functions (BIMFs) extracted by bidimensional variational mode decomposition (BVMD). As an image decomposition algorithm, BVMD represents SAR images by several BIMFs to describe the global and local properties of targets. Furthermore, non-linear correlation information entropy is adopted as a quantitative measure to choose the optimal subset from the multi-level BIMFs, which are assumed to share high correlations. In the classification phase, joint sparse representation is first employed to represent the selected BIMFs and output their corresponding reconstruction error vectors as well as decisions based on the minimum error principle. Afterward, a voting strategy is conducted on their individual decisions to reach a candidate target label. Once the voting decision is judged reliable, the classification process ends and confirms the candidate target label. Otherwise, a linear weighting algorithm is performed on the reconstruction error vectors from different BIMFs, in which the weights are decided by the voting result. Finally, the target label is determined based on the fused reconstruction errors. Experiments are conducted under different situations including the standard operating conditions and extended operating conditions from the moving and stationary target acquisition and recognition dataset. The results of the proposed method are analyzed and compared with other methods to validate its performance.

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Multiset Canonical Correlations Analysis of Bidimensional Intrinsic Mode Functions for Automatic Target Recognition of SAR Images

Cited by 5 publications

References 55 publications

Multitask Sparse Representation of Two-Dimensional Variational Mode Decomposition Components for SAR Target Recognition

Multitask Sparse Representation of Two-Dimensional Variational Mode Decomposition Components for SAR Target Recognition

An SAR Image Automatic Target Recognition Method Based on the Scattering Parameter Gaussian Mixture Model

Robust target recognition in synthetic aperture radar images via correlation analysis and joint decision of multi-level bidimensional intrinsic mode functions by bidimensional variational mode decomposition

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