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a simple semi-automatic approach for land cover classification from multispectral remote sensing imagery一个简单的半自动方法从多光谱遥感图像土地覆盖分类
A Simple Semi-Automatic Approach for Land Cover
Classification from Multispectral Remote Sensing
Imagery
Dong Jiang, Yaohuan Huang*, Dafang Zhuang, Yunqiang Zhu, Xinliang Xu, Hongyan Ren
State Key Lab of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences,
Beijing, China
Abstract
Land cover data represent a fundamental data source for various types of scientific research. The classification of land cover
based on satellite data is a challenging task, and an efficient classification method is needed. In this study, an automatic
scheme is proposed for the classification of land use using multispectral remote sensing images based on change detection
and a semi-supervised classifier. The satellite image can be automatically classified using only the prior land cover map and
existing images; therefore human involvement is reduced to a minimum, ensuring the operability of the method. The
method was tested in the Qingpu District of Shanghai, China. Using Environment Satellite 1(HJ-1) images of 2009 with 30 m
spatial resolution, the areas were classified into five main types of land cover based on previous land cover data and spectral
features. The results agreed on validation of land cover maps well with a Kappa value of 0.79 and statistical area biases in
proportion less than 6%. This study proposed a simple semi-automatic approach for land cover classification by using prior
maps with satisfied accuracy, which integrated the accuracy of visual interpretation and performance of automatic
classification methods. The method can be used for land cover mapping in areas lacking ground reference information or
identifying rapid variation of land cover regions (such as rapid urbanization) with convenience.
Citation: Jiang D, Huang Y, Zhuang D, Zhu Y, X
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