首页|A polarization fusion network with geometric feature emb e dding for SAR ship classification
A polarization fusion network with geometric feature emb e dding for SAR ship classification
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NSTL
Elsevier
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 polar-ization 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. Results on OpenSARShip reveal PFGFE-Net's excel-lent performance. (c) 2021 Elsevier Ltd. All rights reserved.
NSTL
Elsevier
