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http://dx.doi.org/10.7780/kjrs.2012.28.5.2

Classification of Remote Sensing Data using Random Selection of Training Data and Multiple Classifiers  

Park, No-Wook (Department of Geoinformatic Engineering, Inha University)
Yoo, Hee Young (Geoinformatic Engineering Research Institute, Inha University)
Kim, Yihyun (Soil & Fertilizer Management Division, National Academy of Agricultural Science)
Hong, Suk-Young (Soil & Fertilizer Management Division, National Academy of Agricultural Science)
Publication Information
Korean Journal of Remote Sensing / v.28, no.5, 2012 , pp. 489-499 More about this Journal
Abstract
In this paper, a classifier ensemble framework for remote sensing data classification is presented that combines classification results generated from both different training sets and different classifiers. A core part of the presented framework is to increase a diversity between classification results by using both different training sets and classifiers to improve classification accuracy. First, different training sets that have different sampling densities are generated and used as inputs for supervised classification using different classifiers that show different discrimination capabilities. Then several preliminary classification results are combined via a majority voting scheme to generate a final classification result. A case study of land-cover classification using multi-temporal ENVISAT ASAR data sets is carried out to illustrate the potential of the presented classification framework. In the case study, nine classification results were combined that were generated by using three different training sets and three different classifiers including maximum likelihood classifier, multi-layer perceptron classifier, and support vector machine. The case study results showed that complementary information on the discrimination of land-cover classes of interest would be extracted within the proposed framework and the best classification accuracy was obtained. When comparing different combinations, to combine any classification results where the diversity of the classifiers is not great didn't show an improvement of classification accuracy. Thus, it is recommended to ensure the greater diversity between classifiers in the design of multiple classifier systems.
Keywords
Multiple classifier systems; Supervised classification; Classification accuracy; Diversity;
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Times Cited By KSCI : 2  (Citation Analysis)
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