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Xiaohua Tong
Researcher at Tongji University
Publications - 411
Citations - 7381
Xiaohua Tong is an academic researcher from Tongji University. The author has contributed to research in topics: Computer science & Hyperspectral imaging. The author has an hindex of 32, co-authored 332 publications receiving 4855 citations. Previous affiliations of Xiaohua Tong include University of Toronto & Wuhan University.
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Detection and Estimation of Along-Track Attitude Jitter From Ziyuan-3 Three-Line-Array Images Based on Back-Projection Residuals
TL;DR: Experimental results reveal that the frequency of the attitude jitter in the along-track direction ranges between 0.6 and 0.7 Hz, which is consistent with the frequency in the cross- track direction observed in the previous study, which validates the potential of the proposed approach for geometric accuracy improvement with ZY-3 TLA images.
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Dynamic Monitoring of Agricultural Fires in China from 2010 to 2014 Using MODIS and GlobeLand30 Data
TL;DR: MODIS Terra active fire product and GlobeLand30-2010 data showed that the Central and Eastern China regions are the largest contributors to agricultural burning, producing 59%–80% of all the agricultural fires.
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Visualization of large spatial data in networking environments
TL;DR: A novel 3D data model for representing the features of the terrain surface and 3D objects is proposed and paired with a spatial index provides an effective way for powerful access and manipulation of large-scale data sets.
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Tandem catalysts composed of different morphology HZSM-5 and metal oxides for CO2 hydrogenation to aromatics
TL;DR: In this paper , a series of metal oxides (In2O3, Cu-Zn-Al and ZnZrxO with different Zr/Zn and calcined at different temperatures) and different morphologies of spherical, hollow, sheet, and chain HZSM-5 were rationally designed, and then connected with metal oxide and HZ SM-5 in tandem to investigate the reaction conditions and catalytic performance for the direct conversion that from CO2 hydrogenation to aromatics, the reaction mechanism was investigated using In-situ DRIFTS.
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How current and future urban patterns respond to urban planning? An integrated cellular automata modeling approach
TL;DR: A planning-constrained model that integrates cellular automata and genetic algorithm to simulate current and future urban patterns under the spatial constraints of urban planning and predicts the quantity and location of illegal urban development and identifies spatially varying urban growth across planning regulations is proposed.