A simple semi-automatic approach for land cover classification from multispectral remote sensing imagery.
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 lan...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2012-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC3458801?pdf=render |
| _version_ | 1828898376631975936 |
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| author | Dong Jiang Yaohuan Huang Dafang Zhuang Yunqiang Zhu Xinliang Xu Hongyan Ren |
| author_facet | Dong Jiang Yaohuan Huang Dafang Zhuang Yunqiang Zhu Xinliang Xu Hongyan Ren |
| author_sort | Dong Jiang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-13T15:14:16Z |
| format | Article |
| id | doaj.art-0deba1735dc84f3a91db15c985bf3054 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-13T15:14:16Z |
| publishDate | 2012-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-0deba1735dc84f3a91db15c985bf30542022-12-21T23:40:46ZengPublic Library of Science (PLoS)PLoS ONE1932-62032012-01-0179e4588910.1371/journal.pone.0045889A simple semi-automatic approach for land cover classification from multispectral remote sensing imagery.Dong JiangYaohuan HuangDafang ZhuangYunqiang ZhuXinliang XuHongyan RenLand 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.http://europepmc.org/articles/PMC3458801?pdf=render |
| spellingShingle | Dong Jiang Yaohuan Huang Dafang Zhuang Yunqiang Zhu Xinliang Xu Hongyan Ren A simple semi-automatic approach for land cover classification from multispectral remote sensing imagery. PLoS ONE |
| title | A simple semi-automatic approach for land cover classification from multispectral remote sensing imagery. |
| title_full | A simple semi-automatic approach for land cover classification from multispectral remote sensing imagery. |
| title_fullStr | A simple semi-automatic approach for land cover classification from multispectral remote sensing imagery. |
| title_full_unstemmed | A simple semi-automatic approach for land cover classification from multispectral remote sensing imagery. |
| title_short | A simple semi-automatic approach for land cover classification from multispectral remote sensing imagery. |
| title_sort | simple semi automatic approach for land cover classification from multispectral remote sensing imagery |
| url | http://europepmc.org/articles/PMC3458801?pdf=render |
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