Land cover classification based on tolerant rough set
Land cover classification based on tolerant rough set
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DOI：
10.1080/01431160600702368
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发表时间：
2006-07
影响因子：
3.4
通讯作者：
Ouyang Yun;Jianwen Ma
中科院分区：
文献类型：
--
作者：
Ouyang Yun;Jianwen Ma
将容忍粗糙集分类器（TRSC）引入到土地覆盖分类中。TRSC利用容差关系定义待分类对象的容差粗糙集，然后利用其容差粗糙集下近似或边界上各类的相对频率对待分类对象进行分类。根据总体准确率、κ系数、总归一化误分类概率（TNPM）和McNemar检验，TRSC的结果优于最小距离分类器（MDC），与最大似然分类器（MLC）和多层感知器（MLP）的结果相似。
The tolerant rough set classifier (TRSC) was introduced for land cover classification. TRSC uses a tolerance relation to define the tolerant rough set of each object to be classified, and then classifies the object using the relative frequency of each class in the lower approximation or boundary of its tolerant rough set. According to the overall accuracy, the κ coefficient, the total normalized probability of misclassification (TNPM) and McNemar's test, the result of TRSC was better than that of the minimum distance classifier (MDC), and similar to those of the maximum likelihood classifier (MLC) and the multiplayer perceptron (MLP).