%0 Journal Article
%T Exploring the Use of Google Earth Imagery and Object-Based Methods in Land Use/Cover Mapping
%A Qiong Hu
%A Wenbin Wu
%A Tian Xia
%A Qiangyi Yu
%A Peng Yang
%A Zhengguo Li
%A Qian Song
%J Remote Sensing
%D 2013
%I MDPI AG
%R 10.3390/rs5116026
%X Google 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.
%K Google Earth
%K QuickBird
%K land use/cover
%K object-based
%K classification
%U http://www.mdpi.com/2072-4292/5/11/6026