This paper employs transfer-learning to transfer the prior knowledge learned from a simulated SAR dataset to a real SAR dataset.
Oct 22, 2024 · Synthetic Aperture Radar (SAR) target classification is an important branch of SAR image interpretation. The deep learning based SAR target ...
A simple and effective sample spectral regularization method is proposed, which can regularize the singular values of each SAR image feature to improve the ...
SAR Target Classification Based on Sample Spectral Regularization.
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Abstract: Synthetic Aperture Radar (SAR) target classification is an important branch of SAR image interpretation. The deep learning based SAR target ...
SAR Target Classification Based on Sample Spectral Regularization. Remote Sens. 2020, 12, 3628. https://doi.org/10.3390/rs12213628. AMA Style. Liang W, Zhang ...
This paper proposes a robust method for feature-based matching with potential for application to synthetic aperture radar (SAR) automatic target recognition ( ...
Dec 24, 2024 · Designed an innovative network based on Riemannian manifold theory, capturing key frequency domain information while preserving the geometric ...
Synthetic Aperture Radar (SAR) target classification is an important branch of SAR image interpretation. The deep learning based SAR target classification ...
This article discusses how to release the full potential of simulated samples which is used to improve performance of SAR target classifier.
Therefore, this paper proposes a multi-scale attention super-class CNN (MSA-SCNN) for SAR target classification. Firstly, MSA-SCNN combines multi-scale feature ...