NDLI: Random Feature Selection for Decision Tree Classification of Multi-temporal SAR Data

Content Provider	IEEE Xplore Digital Library
Author	Waske, B. Schiefer, S. Braun, M.
Copyright Year	2006
Description	Author affiliation: Center for Remote Sensing of Land Surfaces, Bonn Univ., Bonn (Waske, B.; Schiefer, S.; Braun, M.)
Abstract	The accuracy of supervised land cover classifications depends on variables like the chosen algorithm, adequate training data and the selection of features. It has been shown that classification results can be improved by classifier ensembles. In the present study decision trees have been generated with random selections of all available features and combined into such a multiple classifier. The influence of the number of selected features and the size of the multiple classifiers on classification accuracy is investigated using a set of 14 SAR images. Results of multiple classifiers are always better than those of a decision tree based on all available features. Maximum accuracies were achieved with multiple classifiers that use decision trees based on 70% of the available features. The visual inspection of produced maps underlines the high quality of the results. The area is classified into homogeneous fields with little noise, only.
Starting Page	168
Ending Page	171
File Size	407405
Page Count	4
File Format	PDF
ISBN	0780395107
DOI	10.1109/IGARSS.2006.48
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-07-31
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Decision trees Classification tree analysis Remote sensing Bagging Boosting Spatial resolution Land surface Training data Inspection Support vector machines
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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