A Dual-Polarization Information Guided Network for SAR Ship Classification
Tianwen Zhang, and Xiaoling Zhang
TL;DR
This paper introduces DPIG-Net, a novel neural network architecture that leverages dual-polarization SAR data to improve ship classification accuracy.
Contribution
The paper presents a new dual-polarization guided network specifically designed for SAR ship classification, addressing the challenge of utilizing polarization information effectively.
Findings
Enhanced classification accuracy with dual-polarization data
Effective utilization of polarization information in SAR images
Improved model performance over existing methods
Abstract
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.
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Taxonomy
TopicsAdvanced SAR Imaging Techniques · Synthetic Aperture Radar (SAR) Applications and Techniques