最大似然分类
This algorithm adopted the relative Bhattacharyya distance summation of each feature as the criterion function to select the features which contributed more to the reduction of classification error. These features then could be used for Gaussian maximum likelihood classification.
该算法以各特征的相对Bhattacharyya和作为准则函数选择能有效降低分类错误率的一组特征,最后利用这组特征进行高斯似然分类。
According to a classification experiment on the rugged terrain over the upstream of Hanjiang River Basin where the land use/ cover ground survey data are available, the proposed approach is far superior to the traditional maximum likelihood classification method.
与传统的最大似然法分类结果相比,该方法极大地改善了山区地表覆被分类的精度,得到试验区较为可靠的遥感分类图像。
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