Target Recognition for SAR Images Enhanced by Polarimetric Information

Lin, Huiping; Wang, Haipeng; Xu, Feng; Jin, Ya-Qiu

doi:10.1109/tgrs.2024.3361931

IEEE Trans. Geosci. Remote Sensing

2024

DOI: 10.1109/tgrs.2024.3361931

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Target Recognition for SAR Images Enhanced by Polarimetric Information

Huiping Lin,

Haipeng Wang,

Feng Xu

et al.

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2024

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Cited by 2 publications

References 48 publications

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Interpreting Neural Network Pattern With Pruning for PolSAR Target Recognition

Lin,

Yin,

Yang

et al. 2024

IEEE Trans. Geosci. Remote Sensing

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Interpreting Neural Network Pattern With Pruning for PolSAR Target Recognition

Lin,

Yin,

Yang

et al. 2024

IEEE Trans. Geosci. Remote Sensing

View full text Add to dashboard Cite

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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Target Recognition for SAR Images Enhanced by Polarimetric Information

Cited by 2 publications

References 48 publications

Interpreting Neural Network Pattern With Pruning for PolSAR Target Recognition

Interpreting Neural Network Pattern With Pruning for PolSAR Target Recognition

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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