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Sea Surface Wind Speed Retrieval From Textures in Synthetic Aperture Radar Imagery 期刊论文
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2022, 卷号: 60, 页码: 11
作者:  Zhou, Lizhang;  Zheng, Gang;  Yang, Jingsong;  Li, Xiaofeng;  Zhang, Bin;  Wang, He;  Chen, Peng;  Wang, Yan
Adobe PDF(27025Kb)  |  收藏  |  浏览/下载：241/0  |  提交时间：2022/02/18
Radar polarimetry  Sea surface  Oceanography  Atmospheric modeling  Surface waves  Atmospheric waves  Wind speed  Entropy  gray-level cooccurrence matrix (GLCM)  synthetic aperture radar (SAR)  sea surface wind speed (SSWS)  textures  
A Deep Learning Model to Extract Ship Size From Sentinel-1 SAR Images 期刊论文
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2022, 卷号: 60, 页码: 14
作者:  Ren, Yibin;  Li, Xiaofeng;  Xu, Huan
Adobe PDF(12529Kb)  |  收藏  |  浏览/下载：171/0  |  提交时间：2022/02/18
Marine vehicles  Radar polarimetry  Feature extraction  Synthetic aperture radar  Data mining  Radar imaging  Oceans  Custom loss function  deep learning (DL)  deep neural network (DNN) regression  ship size extraction  synthetic aperture radar (SAR) image  
The Fusion of Physical, Textural, and Morphological Information in SAR Imagery for Hurricane Wind Speed Retrieval Based on Deep Learning 期刊论文
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2022, 卷号: 60, 页码: 13
作者:  Mu, Shanshan;  Li, Xiaofeng;  Wang, Haoyu
Adobe PDF(2103Kb)  |  收藏  |  浏览/下载：233/0  |  提交时间：2022/07/18
Hurricanes  Synthetic aperture radar  Wind speed  Radar polarimetry  Sea surface  Sea measurements  Data models  Deep learning  hurricane wind  synthetic aperture radar (SAR)  
Multilayer Fusion Recurrent Neural Network for Sea Surface Height Anomaly Field Prediction 期刊论文
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2022, 卷号: 60, 页码: 11
作者:  Zhou, Yuan;  Lu, Chang;  Chen, Keran;  Li, Xiaofeng
Adobe PDF(5381Kb)  |  收藏  |  浏览/下载：212/0  |  提交时间：2022/04/12
Computer architecture  Microprocessors  Predictive models  Mathematical models  Sea surface  Data models  Satellites  Deep learning (DL)  field prediction  satellite remote sensing data  sea surface height anomaly (SSHA)  
Environment Monitoring of Shanghai Nanhui Intertidal Zone With Dual-Polarimetric SAR Data Based on Deep Learning 期刊论文
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2022, 卷号: 60, 页码: 18
作者:  Liu, Guangyang;  Liu, Bin;  Zheng, Gang;  Li, Xiaofeng
收藏  |  浏览/下载：118/0  |  提交时间：2023/01/12
Deep convolutional neural networks (DCNNs)  deep learning  environment monitoring  intertidal zone  MB-U-2-ACNet  synthetic aperture radar (SAR) imagery