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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.
Papers
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Journal ArticleDOI
Dynamic PSF-based jitter compensation and quality improvement for push-broom optical images considering terrain relief and the TDI effect.
TL;DR: In this article , a dynamic point spread function (PSF) estimation and iterative image restoration is proposed for push-broom optical images, considering the effect of terrain relief and time delay integration.
Journal ArticleDOI
ICE FLOW VELOCITY MAPPING IN EAST ANTARCTICA USING HISTORICAL IMAGES FROM 1960s TO 1980s: RECENT PROGRESS
S. Luo,Yuan Cheng,Z. Li,Y. Wang,K. Wang,X. Wang,Gang Qiao,W. Ye,Yanjun Li,Menglian Xia,Xiaolan Yuan,Yixiang Tian,Xiaohua Tong,Rongxing Li +13 more
TL;DR: In this article, the authors proposed a method of extracting ice velocity based on the historical optical images from 1960s to 1980s to study the mass change trend in the context of a long period of the East Antarctic Ice Sheet.
Book ChapterDOI
MUNOLD: Landslide Monitoring Using a Spatial Sensor Network
TL;DR: The first version of implementation of UNOLD, an early warning system which enables real-time monitoring of remote landslide areas, was implemented in a down-scaled landslide simulation test site on the campus of Tongji University.
Journal ArticleDOI
A Comparison of Surface Slopes Extracted from ICESat Waveform Data and High Resolution DEM
TL;DR: In this article, surface slope within laser footprint is calculated using the Ice, Cloud, and land Elevation Satellite (ICESat) Geoscience Laser Altimeter System (GLAS) full waveform data and a high resolution Digital Elevation Model (REMA, the Reference Elevation model of Antarctica).
Journal ArticleDOI
A feature extraction and similarity metric-learning framework for urban model retrieval
TL;DR: Both qualitative and quantitative experimental results indicate that the proposed framework can localize and segment a query object from an input image precisely and that the retrieval results are better than those of other related approaches.