Abstract
Nowadays, multiple classifier system is widely used for land cover classification by remote sensing imagery. The performance of combined classifier is closely related to the selection of member classifiers, so it is necessary to analyze the diversity and consistency of member classifiers. In our study, consistency measures are studied and experimented from three levels: general consistency measure, binary prior measure and consistency of errors, and the result shows that it is feasible to find the effective set of member classifiers by some consistency measures. In land cover classification by remotely sensed classifiers, we can select optimal member classifiers by integrating different consistency criterions.
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Du, P., Li, G., Zhang, W., Wang, X., Sun, H. (2009). Consistency Measure of Multiple Classifiers for Land Cover Classification by Remote Sensing Image. In: Benediktsson, J.A., Kittler, J., Roli, F. (eds) Multiple Classifier Systems. MCS 2009. Lecture Notes in Computer Science, vol 5519. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02326-2_40
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DOI: https://doi.org/10.1007/978-3-642-02326-2_40
Publisher Name: Springer, Berlin, Heidelberg
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