Abstract : Decision tree classification algorithms have significant potential for remote sensing data classification. This paper advances to adopt decision tree technologies to classify remote sensing images. First, this paper discussed the algorithms structure and the algorithms theory of decision tree. Second, C4.5 basic theory and boosting technology are explained. The decision tree technologies have several advantages for remote sensing application by virtue of their relatively simple, explicit and intuitive classification structure.
Daoliang Li; Yande Liu; Yingyi Chen. 4th Conference on Computer and Computing Technologies in Agriculture (CCTA), Oct 2010, Nanchang, China. Springer, IFIP Advances in Information and Communication Technology, AICT-344 (Part I), pp.353-358, 2011, Computer and Computing Technologies in Agriculture IV. 〈10.1007/978-3-642-18333-1_41〉
https://hal.inria.fr/hal-01559629
Contributeur : Hal Ifip
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Soumis le : lundi 10 juillet 2017 - 17:28:47
Dernière modification le : mardi 18 juillet 2017 - 15:29:31
Document(s) archivé(s) le : mercredi 24 janvier 2018 - 16:52:58
Lihua Jiang, Wensheng Wang, Xiaorong Yang, Nengfu Xie, Youping Cheng. Classification Methods of Remote Sensing Image Based on Decision Tree Technologies. Daoliang Li; Yande Liu; Yingyi Chen. 4th Conference on Computer and Computing Technologies in Agriculture (CCTA), Oct 2010, Nanchang, China. Springer, IFIP Advances in Information and Communication Technology, AICT-344 (Part I), pp.353-358, 2011, Computer and Computing Technologies in Agriculture IV. 〈10.1007/978-3-642-18333-1_41〉. 〈hal-01559629〉
