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Open AccessArticle

Self-Training Classification Framework with Spatial-Contextual Information for Local Climate Zones

by Nan Zhao 1,2, Ailong Ma 1,2,*, Yanfei Zhong 1,2, Ji Zhao 3 and Liqin Cao 4
1
The State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
2
Hubei Provincial Engineering Research Center of Natural Resources Remote Sensing Monitoring, Wuhan University, Wuhan 430079, China
3
College of Computer Science, China University of Geosciences, Wuhan 430074, China
4
School of Printing and Packing, Wuhan University, Wuhan 430079, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(23), 2828; https://doi.org/10.3390/rs11232828
Received: 11 October 2019 / Revised: 17 November 2019 / Accepted: 26 November 2019 / Published: 28 November 2019
(This article belongs to the Special Issue Application of Remote Sensing in Urban Climatology)
Local climate zones (LCZ) have become a generic criterion for climate analysis among global cities, as they can describe not only the urban climate but also the morphology inside the city. LCZ mapping based on the remote sensing classification method is a fundamental task, and the protocol proposed by the World Urban Database and Access Portal Tools (WUDAPT) project, which consists of random forest classification and filter-based spatial smoothing, is the most common approach. However, the classification and spatial smoothing lack a unified framework, which causes the appearance of small, isolated areas in the LCZ maps. In this paper, a spatial-contextual information-based self-training classification framework (SCSF) is proposed to solve this LCZ classification problem. In SCSF, conditional random field (CRF) is used to integrate the classification and spatial smoothing processing into one model and a self-training method is adopted, considering that the lack of sufficient expert-labeled training samples is always a big issue, especially for the complex LCZ scheme. Moreover, in the unary potentials of CRF modeling, pseudo-label selection using a self-training process is used to train the classifier, which fuses the regional spatial information through segmentation and the local neighborhood information through moving windows to provide a more reliable probabilistic classification map. In the pairwise potential function, SCSF can effectively improve the classification accuracy by integrating the spatial-contextual information through CRF. The experimental results prove that the proposed framework is efficient when compared to the traditional mapping product of WUDAPT in LCZ classification. View Full-Text
Keywords: local climate zones (LCZ); spatial-contextual information; self-training; conditional random fields (CRF) local climate zones (LCZ); spatial-contextual information; self-training; conditional random fields (CRF)
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MDPI and ACS Style

Zhao, N.; Ma, A.; Zhong, Y.; Zhao, J.; Cao, L. Self-Training Classification Framework with Spatial-Contextual Information for Local Climate Zones. Remote Sens. 2019, 11, 2828.

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