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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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Modeling urban growth using spatially heterogeneous cellular automata models: Comparison of spatial lag, spatial error and GWR
TL;DR: The results demonstrate that spatial regression can help produce accurate simulations of urban dynamics featured by spatial heterogeneity, either implicitly or explicitly, and should help select appropriate CA models of urban growth in different terrain and socioeconomic contexts.
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An Improved Weighted Total Least Squares Method with Applications in Linear Fitting and Coordinate Transformation
TL;DR: An improved weighted total least squares (IWTLS) method for the EIV model with applications in linear fitting and coordinate transformation is presented in this article. But this method is not suitable for the case of linear orthogonal regression problems.
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A new cellular automata framework of urban growth modeling by incorporating statistical and heuristic methods
Yongjiu Feng,Xiaohua Tong +1 more
TL;DR: A new geographical cellular automata (CA) modeling framework, named UrbanCA, is developed through reconstructing the essential CA structure and incorporating nonspatial, spatial, and heuristic approaches to simulate the dynamic urban growth and assess the resulting natural and socioeconomic impacts.
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Unsupervised Change Detection in Multispectral Remote Sensing Images via Spectral-Spatial Band Expansion
TL;DR: This paper proposes to use unsupervised band expansion techniques to generate artificial spectral and spatial bands to enhance the change representation and discrimination for change detection (CD) from multispectral images.
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A Three-Step Approach for TLS Point Cloud Classification
Zhuqiang Li,Liqiang Zhang,Xiaohua Tong,Bo Du,Yuebin Wang,Liang Zhang,Zhenxin Zhang,Hao Liu,Jie Mei,Xiaoyue Xing,P. Takis Mathiopoulos +10 more
TL;DR: The proposed method has been validated on three urban TLS point clouds, and the experimental results demonstrate that it outperforms the state-of-the-art method in classification accuracy for buildings, trees, pedestrians, and cars.