文章基本信息

标题：Land Use Classification using Support Vector Machine and Maximum Likelihood Algorithms by Landsat 5 TM Images
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作者：Abbas TAATI ; Fereydoon SARMADIAN ; Amin MOUSAVI 等
期刊名称：Walailak Journal of Science and Technology (WJST)
印刷版ISSN：2228-835X
出版年度：2014
卷号：12
期号：8
页码：681-687
语种：English
出版社：Institute of Research and Development, Walailak University.
摘要：Nowadays, remote sensing images have been identified and exploited as the latest information to study land cover and land uses. These digital images are of significant importance, since they can present timely information, and capable of providing land use maps. The aim of this study is to create land use classiﬁcation using a support vector machine (SVM) and maximum likelihood classifier (MLC) in Qazvin, Iran, by TM images of the Landsat 5 satellite. In the pre-processing stage, the necessary corrections were applied to the images. In order to evaluate the accuracy of the 2 algorithms, the overall accuracy and kappa coefficient were used. The evaluation results verified that the SVM algorithm with an overall accuracy of 86.67 % and a kappa coefficient of 0.82 has a higher accuracy than the MLC algorithm in land use mapping. Therefore, this algorithm has been suggested to be applied as an optimal classifier for extraction of land use maps due to its higher accuracy and better consistency within the study area. doi: 10.14456/WJST.2015.33
关键词：Remote sensing, satellite, overall accuracy, kappa coefficient