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Empirical comparison of machine learning algorithms for image texture classification with application to vegetation management in power line corridors

By Zhengrong Li, Yuee Liu, Ross Hayward and Rodney Walker

Abstract

This paper reports on the empirical comparison of seven machine learning algorithms in texture classification with application to vegetation management in power line corridors. Aiming at classifying tree species in power line corridors, object-based method is employed. Individual tree crowns are segmented as the basic classification units and three classic texture features are extracted as the input to the classification algorithms. Several widely used performance metrics are used to evaluate the classification algorithms. The experimental results demonstrate that the classification performance depends on the performance matrix, the characteristics of datasets and the feature used

Topics: 080100 ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING, 090905 Photogrammetry and Remote Sensing, Classification, Texture Feature, Machine Learning, Object-based Image Analysis, Vegetation
Publisher: ISPRS
Year: 2010
OAI identifier: oai:eprints.qut.edu.au:39271
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