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.
田绍鸿, 张显峰. 采用随机森林法的天绘数据干旱区城市土地覆盖分类[J]. 国土资源遥感, 2016, 28(1): 43-49.
TIAN Shaohong, ZHANG Xianfeng. Random forest classification of land cover information of urban areas in arid regions based on TH-1 data. REMOTE SENSING FOR LAND & RESOURCES, 2016, 28(1): 43-49.
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