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REMOTE SENSING FOR LAND & RESOURCES    2016, Vol. 28 Issue (1) : 43-49     DOI: 10.6046/gtzyyg.2016.01.07
Technology and Methodology |
Random forest classification of land cover information of urban areas in arid regions based on TH-1 data
TIAN Shaohong, ZHANG Xianfeng
Institute of Remote Sensing and GIS, Peking University, Beijing 100871, China
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Abstract  

Random-forest classification(RFC)method was used to extract the land cover information from the TH-1 satellite remotely sensed multispectral data in Beitun Town and its adjacent areas within the arid region of Altay,Xinjiang. Owing to the mixture of the impervious covers and the exposed soils inside the city, the textural and vegetation features were derived from the TH-1 panchromatic image and multispectral bands and subsequently applied to creating optimal feature set so as to implement the RFC classification. The optimized classifier can achieve better identification of some confused land cover classes. The results show that the RFC possesses higher accuracy than the conventional maximum likelihood classification(MLC)with the same TH-1 image, with their total accuracy being 82.26% and 72.61%, respectively. In addition, favorable applicability is observed in the land cover classification in the arid urban region using optimized combined multi-feature methods, which can provide land cover information for the urban development and planning in the medium and small cities of Xinjiang.

Keywords high resolution remote sensing      mine environment      division of governance      comprehensive governance      countermeasures     
:  TP751.1  
Issue Date: 27 November 2015
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YANG Xianhua
HUANG Jie
TIAN Li
LIU Zhi
HAN Lei
Cite this article:   
YANG Xianhua,HUANG Jie,TIAN Li, et al. Random forest classification of land cover information of urban areas in arid regions based on TH-1 data[J]. REMOTE SENSING FOR LAND & RESOURCES, 2016, 28(1): 43-49.
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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2016.01.07     OR     https://www.gtzyyg.com/EN/Y2016/V28/I1/43

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