Proceedings of the 2013 the International Conference on Remote Sensing, Environment and Transportation Engineering (RSETE 2013)

Research and application of Object-oriented remote sensing image classification based on decision tree

Authors
Dou Peng, Zhai Liang, Sang Huiyong, Xie Wenhan
Corresponding Author
Dou Peng
Available Online August 2013.
DOI
10.2991/rsete.2013.66How to use a DOI?
Keywords
remote sensing image classification; object-oriented; decision tree classification; C5.0 algorithm
Abstract

The paper mainly introduced the basic principle and method of Object-oriented classification technology and made studies and research on decision tree classification technology. In addition, the remote sensing image was classified with Object-oriented way by using C5.0 algorithm, and compared with the result based on pixels classification and the result of SVM Object-oriented classification, which has improved that the accuracy and effectiveness of decision tree classification can be used in Object-oriented remote sensing image classification.

Copyright
© 2013, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

Volume Title
Proceedings of the 2013 the International Conference on Remote Sensing, Environment and Transportation Engineering (RSETE 2013)
Series
Advances in Intelligent Systems Research
Publication Date
August 2013
ISBN
10.2991/rsete.2013.66
ISSN
1951-6851
DOI
10.2991/rsete.2013.66How to use a DOI?
Copyright
© 2013, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Dou Peng
AU  - Zhai Liang
AU  - Sang Huiyong
AU  - Xie Wenhan
PY  - 2013/08
DA  - 2013/08
TI  - Research and application of Object-oriented remote sensing image classification based on decision tree
BT  - Proceedings of the 2013 the International Conference on Remote Sensing, Environment and Transportation Engineering (RSETE 2013)
PB  - Atlantis Press
SP  - 270
EP  - 273
SN  - 1951-6851
UR  - https://doi.org/10.2991/rsete.2013.66
DO  - 10.2991/rsete.2013.66
ID  - Peng2013/08
ER  -