Volume XXXIX-B7
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XXXIX-B7, 421-426, 2012
https://doi.org/10.5194/isprsarchives-XXXIX-B7-421-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XXXIX-B7, 421-426, 2012
https://doi.org/10.5194/isprsarchives-XXXIX-B7-421-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.

  01 Aug 2012

01 Aug 2012

LARGE AREA LAND COVER CLASSIFICATION WITH LANDSAT ETM+ IMAGES BASED ON DECISION TREE

L. Zhai, J. Sun, H. Sang, G. Yang, and Y. Jia L. Zhai et al.
  • Key Laboratory of Geo-Informatics of NASG, Chinese Academy of Surveying and Mapping, Beijing, China

Keywords: land cover classification, decision tree, C5.0, MLC

Abstract. Traditional land classification techniques for large areas that use LANDSAT TM imagery are typically limited to the fixed spatial resolution of the sensors. For modeling habitat characteristics is often difficult when a study area is large and diverse and complete sampling of environmental variables is unrealistic. We also did some researches on this field, in this paper we firstly introduced the decision tree classification based on C5.0, and then introduced the classification workflow. The study results were compared with the Maximum Likelihood Classification result. Victoria of Australia was as the study area, the LANDSAT ETM+ images were used to classify. Experiments show that the decision tree classification method based on C5.0 is better.