A Successive Decision Tree Approach to Mining Remotely Sensed Image Data

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Author(s): Jianting Zhang (University of New Mexico, USA), Wieguo Liu (University of Toledo, USA)and Le Gruenwald (University of Oklahoma, USA)
Copyright: 2007
Pages: 15
Source title: Knowledge Discovery and Data Mining: Challenges and Realities
Source Author(s)/Editor(s): Xingquan Zhu (University of Vermont, USA)and Ian Davidson (State University of New York at Albany, USA)
DOI: 10.4018/978-1-59904-252-7.ch006

Keywords: Data Mining and Databases / Information Science Reference / Knowledge Discovery / Library & Information Science

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Abstract

Decision trees (DT) has been widely used for training and classification of remotely sensed image data due to its capability to generate human interpretable decision rules and its relatively fast speed in training and classification. This chapter proposes a successive decision tree (SDT) approach where the samples in the ill-classified branches of a previous resulting decision tree are used to construct a successive decision tree. The decision trees are chained together through pointers and used for classification. SDT aims at constructing more interpretable decision trees while attempting to improve classification accuracies. The proposed approach is applied to two real remotely sensed image datasets for evaluations in terms of classification accuracy and interpretability of the resulting decision rules.

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A Successive Decision Tree Approach to Mining Remotely Sensed Image Data

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