The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XXXIX-B7
https://doi.org/10.5194/isprsarchives-XXXIX-B7-421-2012
https://doi.org/10.5194/isprsarchives-XXXIX-B7-421-2012
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

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.