Land cover information extraction from remote sensing images using object-based image analysis method integrated with decision tree
SUN Yuyi1,2, ZHAO Junli1,3, WANG Miaomiao1, LIU Yong1
1. College of Earth and Environment Sciences, Lanzhou University, Lanzhou 730000, China;
2. Map Institute of Gansu, Lanzhou 730000, China;
3. 61175 Troops of PLA, Nanjing 210000, China
Object-based image analysis, which has been developed rapidly over the last decades, performs advantageous over classic pixel-based image classification. One of the key problems within this paradigm is to automatically build robust and transferable rule sets for segment classification. It has been identified promisingly to develop rule sets by means of decision tree based on data mining. The authors suggest a decision tree model integrated with J48 algorithm embedded in Weka to select parameters from a set of spectral, textural and terrain features relevant to rule sets for segment classification. Based on this method, the authors used Landsat5 TM image data and ASTER digital elevation model to establish land cover classification in the study area, i.e., Baicaoyuan area in Huining county, Gansu Province. Rule sets developed in this way perform acceptable robustness and transferability. Accuracy assessment proves that this method has significantly higher classification accuracy than other pixel-based methods based on employing maximum likelihood and objected-based nearest neighbor logic.
孙宇翼, 赵军利, 王苗苗, 刘勇. 基于J48决策树的面向对象方法的土地覆被信息提取[J]. 国土资源遥感, 2016, 28(4): 156-163.
SUN Yuyi, ZHAO Junli, WANG Miaomiao, LIU Yong. Land cover information extraction from remote sensing images using object-based image analysis method integrated with decision tree. REMOTE SENSING FOR LAND & RESOURCES, 2016, 28(4): 156-163.
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