Mixed ground objects in complex basin are easily interfered by background, and foreground mixed in different ground objects is difficult to be distinguished by one rule. In this paper, the authors discuss the classification rule model of common ground feature in different mixed backgrounds. With Landsat8 images as the data source, level 1 tributary of Zhengshui River basin in Xiangjiang River basin and the three big cities of the Yangtze River delta as the study areas, the authors adopted the maximum likelihood method to conduct a preliminary classification. Based on analyzing the spectral characteristics of mixed feature, the authors built the classification decision tree of mixed ground feature to identify water, artificial construction, farmland, bare land, forest land and bare rock. The results obtained by the authors show that the overall accuracy of the Zhengshui River is about 88.21%, which is higher than the supervised classification accuracy of 79.68%, and the overall accuracy of other three cities along the Yangtze River is higher than 92%. It is shown that the classification model for mixed subjects can improve the accuracy of the same ground objects with different backgrounds.
杨宇晖, 颜梅春, 李致家, 余青, 陈贝贝. 南方丘陵地区复杂地表“同物异谱”分类处理模型[J]. 国土资源遥感, 2016, 28(2): 79-83.
YANG Yuhui, YAN Meichun, LI Zhijia, YU Qing, CHEN Beibei. Classification model for "same subject with different spectra" on complicated surface in Southern hilly areas. REMOTE SENSING FOR LAND & RESOURCES, 2016, 28(2): 79-83.
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