Space-Frequency Fusion Dual-Branch Convolutional Neural Networks for Significant Wave Height Retrieval From GF-3 SAR Data

Space-Frequency Fusion Dual-Branch Convolutional Neural Networks for Significant Wave Height Retrieval From GF-3 SAR Data

Deep learning in synthetic aperture radar (SAR) sea state retrieval is becoming increasingly prevalent. In current studies, convolutional neural networks (CNNs) are widely employed to extract either deep space features from normalized radar cross section (NRCS) of SAR images or deep frequency featur...

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Bibliographic Details
Main Authors: Xuan Jin, Yawei Zhao, Xin Zhang, Yanlei Du, Jinsong Chong
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Subjects:
Dual-branch convolutional neural network (DB-CNN)
significant wave height (SWH) retrieval
space-frequency fusion
synthetic aperture radar (SAR)
Online Access:https://ieeexplore.ieee.org/document/10966200/
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https://ieeexplore.ieee.org/document/10966200/

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