#5815. A polarization fusion network with geometric feature embedding for SAR ship classification

July 2026publication date
Proposal available till 15-05-2025
4 total number of authors per manuscript0 $

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Journal’s subject area:
Signal Processing;
Software;
Artificial Intelligence;
Computer Vision and Pattern Recognition;
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Abstract:
Current synthetic aperture radar (SAR) ship classifiers using convolutional neural networks (CNNs) offer state-of-the-art performance. Yet, they still have two defects potentially hindering accuracy progress – polarization insufficient utilization and traditional feature abandonment. Therefore, we propose a polarization fusion network with geometric feature embedding (PFGFE-Net) to solve them. PFGFE-Net achieves the polarization fusion (PF) from the input data, feature-level, and decision-level. Moreover, the geometric feature embedding (GFE) enriches expert experience.
Keywords:
Convolutional neural network; Geometric feature embedding (GFE); Polarization fusion (PF); Ship classification; Synthetic aperture radar (SAR)

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