LARGE AREA LAND COVER CLASSIFICATION WITH LANDSAT ETM+ IMAGES BASED ON DECISION TREE
Other literature type
(issn: 2194-9034, eissn: 2194-9034)
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.