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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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Global land cover mapping at 30 m resolution: A POK-based operational approach

TL;DR: In this article, an approach based on the integration of pixel-and object-based methods with knowledge (POK-based) has been developed to handle the classification process of 10 land cover types, i.e., firstly each class identified in a prioritized sequence and then results are merged together.
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Modeling dynamic urban growth using cellular automata and particle swarm optimization rules

TL;DR: An improved cellular automata model of urban growth based on particle swarm optimization (PSO) approach with inertia weight is presented, demonstrating that the PSO-CA model outperforms other spatial statistical based CA models.
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Mapping essential urban land use categories in China (EULUC-China): preliminary results for 2018

Peng Gong, +71 more
TL;DR: For example, in this article, the authors present a set of urban land use maps at the national and global scales that are derived from the same or consistent data sources with similar or compatible classification systems and mapping methods.
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A Multiscale and Hierarchical Feature Extraction Method for Terrestrial Laser Scanning Point Cloud Classification

TL;DR: A novel multiscale and hierarchical framework is introduced, which describes the classification of TLS point clouds of cluttered urban scenes, and novel features of point clusters are constructed by employing the latent Dirichlet allocation (LDA).
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Building-damage detection using pre- and post-seismic high-resolution satellite stereo imagery: A case study of the May 2008 Wenchuan earthquake

TL;DR: Wang et al. as mentioned in this paper presented an approach for the detection of buildings that have collapsed in an earthquake based on 3D geometric changes, particularly height change of the buildings, using pre- and post-seismic IKONOS stereo image pairs.