26 July 2007 A land-cover classifier using tolerant rough set and multi-class SVM
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Abstract
A simple approach for incorporating tolerant rough sets (TRS) into a multi-class support vector machine (SVM) classifier for land-cover classification was presented. TRS was used to perform the sample preprocessing of the original samples set to reduce the uncertainty of sample set and make the influence of the uncertainty from sample set on the final classification accuracy least. SVM was employed after the TRS preprocessing. An application of the integrated classifiers using an ETM+ remote sensing image has been presented. The classification results were compared with those of only-SVM classifier. According to the overall accuracy and the k coefficient, the result of integrated classifier with TRS and SVM is better than that of only-SVM classifier in the experiment.
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Xincai Wu, Xincai Wu, Fujiang Liu, Fujiang Liu, Bassam-Al F. Bassam, Bassam-Al F. Bassam, Yan Guo, Yan Guo, } "A land-cover classifier using tolerant rough set and multi-class SVM", Proc. SPIE 6752, Geoinformatics 2007: Remotely Sensed Data and Information, 67522Z (26 July 2007); doi: 10.1117/12.761281; https://doi.org/10.1117/12.761281
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