A Dual-Polarization Information Guided Network for SAR Ship Classification
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Abstract (arXiv)
How to fully utilize polarization to enhance synthetic aperture radar (SAR) ship classification remains an unresolved issue. Thus, we propose a dual-polarization information guided network (DPIG-Net) to solve it.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 10 of its 64 references that are in the map. First posted on arXiv 11 Jul 2022.
Builds on
10 of 64 references.
References · 10
- 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 7,005
- 2017Squeeze-and-Excitation Networkscited by 4,789
- 2015Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classificationcited by 3,611
- 2018CBAM: Convolutional Block Attention Modulecited by 2,477
- 2018Residual Dense Network for Image Super-Resolutioncited by 799
- 2017Deep High Dynamic Range Imaging with Large Foreground Motionscited by 99
- 2022A Mask Attention Interaction and Scale Enhancement Network for SAR Ship Instance Segmentationcited by 7
- 2021New SAR target recognition based on YOLO and very deep multi-canonical correlation analysiscited by 5
- 2020Balance Scene Learning Mechanism for Offshore and Inshore Ship Detection in SAR Imagescited by 4
- 2022Shadow-Background-Noise 3D Spatial Decomposition Using Sparse Low-Rank Gaussian Properties for Video-SAR Moving Target Shadow Enhancementcited by 1
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Citing papers per year: 2025: 2.
2 papers.