Comparison of BPNN and Dual-Branch CNN for Significant Wave Height Estimation From Polarimetric Gaofen-3 SAR Wave Mode Data

The present study utilizes the backward propagation neural network (BPNN) and the dual-branch convolutional neural network (DB-CNN) algorithms to construct models for estimating significant wave height (SWH) from polarimetric Gaofen-3 SAR wave mode data, using a dataset of 11 164 images that are col...

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Auteurs principaux:	Qiushuang Yan, Chenqing Fan, Junmin Meng, Tianran Song, Jie Zhang, Weifu Sun
Format:	Artigo
Langue:	Inglês
Publié:	IEEE 2024-01-01
Collection:	IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Sujets:	Backward propagation neural network (BPNN) dual-branch convolutional neural network (DB-CNN) polarimetric Gaofen-3 synthetic aperture radar (SAR) wave mod)e significant wave height (SWH)
Accès en ligne:	https://ieeexplore.ieee.org/document/10517375/
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