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Large Area Land Cover Classification with Landsat Etm+ Images Based on Decision Tree : Volume Xxxix-b7, Issue 1 (01/08/2012)

By Zhai, L.

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Book Id: WPLBN0004016882
Format Type: PDF Article :
File Size: Pages 6
Reproduction Date: 2015

Title: Large Area Land Cover Classification with Landsat Etm+ Images Based on Decision Tree : Volume Xxxix-b7, Issue 1 (01/08/2012)  
Author: Zhai, L.
Volume: Vol. XXXIX-B7, Issue 1
Language: English
Subject: Science, Isprs, International
Collections: Periodicals: Journal and Magazine Collection, Copernicus Publications
Historic
Publication Date:
2012
Publisher: Copernicus Publications, Göttingen, Germany
Member Page: Copernicus Publications

Citation

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Yang, G., Zhai, L., Sang, H., Sun, J., & Jia, Y. (2012). Large Area Land Cover Classification with Landsat Etm+ Images Based on Decision Tree : Volume Xxxix-b7, Issue 1 (01/08/2012). Retrieved from http://www.ebooklibrary.org/


Description
Description: Key Laboratory of Geo-Informatics of NASG, Chinese Academy of Surveying and Mapping, Beijing, China. 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.

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

 

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