G
Gang Zheng
Researcher at Ontario Ministry of Natural Resources
Publications - 72
Citations - 943
Gang Zheng is an academic researcher from Ontario Ministry of Natural Resources. The author has contributed to research in topics: Wind speed & Synthetic aperture radar. The author has an hindex of 12, co-authored 64 publications receiving 420 citations. Previous affiliations of Gang Zheng include State Oceanic Administration.
Papers
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Journal ArticleDOI
Deep-learning-based information mining from ocean remote-sensing imagery
Xiaofeng Li,Bin Liu,Gang Zheng,Yibin Ren,Shuangshang Zhang,Liu Yingjie,Le Gao,Liu Yuhai,Bin Zhang,Fan Wang +9 more
TL;DR: This review paper first systematically reviewed two deep-learning frameworks that carry out ocean remote-sensing-image classifications and then presented eight typical applications in ocean internal-wave/eddy/oil-spill/coastal-inundation/sea-ice/green-algae/ship/coral-reef mapping from different types of oceanRemote-Sensing imagery to show how effective these deep- learning frameworks are.
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Purely satellite data-driven deep learning forecast of complicated tropical instability waves.
TL;DR: This study demonstrates the strong potential of the satellite data–driven deep learning model as an alternative to traditional numerical models for forecasting oceanic phenomena.
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Coastal Inundation Mapping From Bitemporal and Dual-Polarization SAR Imagery Based on Deep Convolutional Neural Networks
Bin Liu,Xiaofeng Li,Gang Zheng +2 more
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Study of the propagation direction of the internal waves in the South China Sea using satellite images
TL;DR: Based on the analysis of more than 2 500 synthetic aperture radar (SAR) and optical satellite images, the internal wave propagation in the whole South China Sea was investigated systematically as mentioned in this paper.
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Development of a Gray-Level Co-Occurrence Matrix-Based Texture Orientation Estimation Method and Its Application in Sea Surface Wind Direction Retrieval From SAR Imagery
TL;DR: A gray-level co-occurrence matrix (GLCM)-based method was developed for better texture orientation estimation in remote sensing imagery and achieves better SSWD retrieval accuracy than commonly used Fourier transform- and gradient-based methods.