An Adaboost Based Method for Dynamic Extraction of Urban Land Use
Cover with Remote Sensing Images
LI Rui 1,2,4, WANG Juan-le 2, REN Zheng-chao 2,3
1.College of Environment and Planning, Henan University, Kaifeng 475000, China； 2.State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, CAS, Beijing 100101, China； 3.College of grass industry, Gansu Agricultural University, Lanzhou 730070, China； 4.The 75711 Troop, PLA, Guangzhou 510515, China
The problem how to combine the low precision urban land use cover classifiers to get higher precision is dealt with in this paper. Using 2007 Shanghai CBERS (China-Brazil Earth Resources Satellite) images, the authors adopted the AdaBoost combination classifier, which can combine spectral feature information, texture structure information and decision tree classier to improve the classification precision. The experiment results show that a notable improvement of the classification precision of urban land use cover can be achieved by using AdaBoost algorithm.
基于AdaBoost算法的城镇建设用地遥感动态提取研究[J]. 国土资源遥感, 2010, 22(2): 86-90.
LI Rui, WANG Juan-Le, REN Zheng-Chao. An Adaboost Based Method for Dynamic Extraction of Urban Land Use
Cover with Remote Sensing Images. REMOTE SENSING FOR LAND & RESOURCES, 2010, 22(2): 86-90.
2010, Vol. 22 
