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Accuracy of Classified Imagery without using Reference Data Set

机译:不使用参考数据集的分类图像的准确性

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for the validation aspect the estimation of accuracy of classifiers is significant factor of analyzing the result of classifiers. If the pixel found in single class. So we prefer the hard classification. But satellite data does not find in single class. The pixel of satellite data belongs more than one class. So in that case we prefer the soft classification. The classification is only one parameter and other parameter is assessment of accuracy and we know without assessment of accuracy classification of classifiers is incomplete. There are several method to find out the the accuracy of classifiers like MLC, FERM, RMSE, and Entropy. Accuracy results are generated for soft classified data set from AWiFS, LISS-III and LISS-IV sensor. The output results from tool used to investigation effect of sampling on assessed accuracy
机译:对于验证方面,分类器准确性的估计是分析分类器结果的重要因素。如果发现像素在单个类中。因此,我们更喜欢硬分类。但是,卫星数据无法在单一类别中找到。卫星数据的像素属于多个类别。因此,在这种情况下,我们更喜欢软分类。分类只是一个参数,其他参数是对准确性的评估,我们知道,没有评估准确性,分类器的分类是不完整的。有几种方法可以找出分类器(如MLC,FERM,RMSE和熵)的准确性。对于来自AWiFS,LISS-III和LISS-IV传感器的软分类数据集,将生成准确性结果。工具用于调查抽样对评估准确性的影响的输出结果

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