Automatic target recognition method based on polsar images with circular polarimetric basis conversion

Ohno, Shouhei; Kidera, Shouhei; Kirimoto, Tetsuo

doi:10.1109/igarss.2015.7326509

Cited by 2 publications

(1 citation statement)

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“…Accordingly, POLSAR has been widely applied in various earth observation applications (e.g., target detection [10] and terrain classification [11]). Besides, several studies [12]- [14] have found that POLSAR has considerable application potential and value in stationary ground target recognition.…”

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confidence: 99%

POLSAR Target Recognition Using a Feature Fusion Framework Based on Monogenic Signal and Complex-Valued Nonlocal Network

Zhang

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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With the continuous development of synthetic aperture radar (SAR) systems, multi-polarization information has been increasingly applied to numerous fields, and automatic target recognition (ATR) in polarimetric SAR (POLSAR) has been recognized as vital problem. The SAR recognition methods can primarily fall into handcrafted feature-based algorithms and deep learning algorithms. The former exhibits excellent interpretability but insufficient generalization; the latter achieves stronger representational ability but relies on a considerable number of samples. To solve above problems, a feature fusion framework is proposed in this paper based on monogenic signal and complex-valued non-local network (CVNLNet) for POLSAR target recognition. The proposed feature fusion framework effectively uses the complementarity of handcrafted features and deep features, while making up for the disadvantages of single feature-based methods. First, a Mono-BOVW model is proposed based on monogenic signal and bag-of-visual-words (BOVW) model to extract handcrafted features, which can more fully mine the information covered in POL-SAR data in multi-scale space. Moreover, CVNLNet is built for deep feature extraction to use both the amplitude and phase covered in POLSAR data. Next, a kernel discrimination correlation analysis algorithm (KDCA) is proposed to jointly analyze and transform the two features, so as to remove redundant information while retaining effective and discriminative information. Experiments on the MSTAR dataset and the GOTCHA dataset show that the proposed framework has superior performance on single polarimetric and fully polarimetric datasets.

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confidence: 99%