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Exploring the Use of Google Earth Imagery and Object-Based Methods in Land Use/Cover Mapping
AbstractGoogle Earth (GE) releases free images in high spatial resolution that may provide some potential for regional land use/cover mapping, especially for those regions with high heterogeneous landscapes. In order to test such practicability, the GE imagery was selected for a case study in Wuhan City to perform an object-based land use/cover classification. The classification accuracy was assessed by using 570 validation points generated by a random sampling scheme and compared with a parallel classification of QuickBird (QB) imagery based on an object-based classification method. The results showed that GE has an overall classification accuracy of 78.07%, which is slightly lower than that of QB. No significant difference was found between these two classification results by the adoption of Z-test, which strongly proved the potentials of GE in land use/cover mapping. Moreover, GE has different discriminating capacity for specific land use/cover types. It possesses some advantages for mapping those types with good spatial characteristics in terms of geometric, shape and context. The object-based method is recommended for imagery classification when using GE imagery for mapping land use/cover. However, GE has some limitations for those types classified by using only spectral characteristics largely due to its poor spectral characteristics.
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Hu, Q.; Wu, W.; Xia, T.; Yu, Q.; Yang, P.; Li, Z.; Song, Q. Exploring the Use of Google Earth Imagery and Object-Based Methods in Land Use/Cover Mapping. Remote Sens. 2013, 5, 6026-6042.View more citation formats
Hu Q, Wu W, Xia T, Yu Q, Yang P, Li Z, Song Q. Exploring the Use of Google Earth Imagery and Object-Based Methods in Land Use/Cover Mapping. Remote Sensing. 2013; 5(11):6026-6042.Chicago/Turabian Style
Hu, Qiong; Wu, Wenbin; Xia, Tian; Yu, Qiangyi; Yang, Peng; Li, Zhengguo; Song, Qian. 2013. "Exploring the Use of Google Earth Imagery and Object-Based Methods in Land Use/Cover Mapping." Remote Sens. 5, no. 11: 6026-6042.
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