进行遥感影像分类或进行GIS动态模拟时，需要评价结果的精度，而进行评价精度的方法主要有混淆矩阵、总体分类精度、Kappa系数、多分误差、漏分误差、每一类的生产者精度（制图精度）和用户精度。

    1、混淆矩阵（Confusion Matrix）: 主要用于比较分类结果和地表真实信息，可以把分类结果的精度显示在一个混淆矩阵里面。混淆矩阵是通过将每个地表真实像元的位置和分类与分类图象中的相应位置和分类像比较计算的。混淆矩阵的每一列代表了地面参考验证信息，每一列中的数值等于地表真实像元在分类图象中对应于相应类别的数量；混淆矩阵的每一行代表了遥感数据的分类信息，每一行中的数值等于遥感分类像元在地表真实像元相应类别中的数量。

如有50个样本数据，这些数据分成3类，每类50个。分类结束后得到的混淆矩阵为：

43  5  2

2  45  3

0   1 49

则第1行的数据说明有43个样本正确分类，有5样本本应该属于第1类，却错误分到了第二类，有2个样本本应属于第一类，而错误的分到第三类。
    2、总体分类精度（Overall Accuracy）: 等于被正确分类的像元总和除以总像元数，地表真实图像或地表真实感兴趣区限定了像元的真实分类。被正确分类的像元沿着混淆矩阵的对角线分布，它显示出被分类到正确地表真实分类中的像元数。像元总数等于所有地表真实分类中的像元总和。
    3、Kappa系数：

     The Kappa Index of Agreement (K): this is an important index that the crossclassification outputs. It measures the association between the two input images and helps to evaluate the output image. Its values range from -1 to +1 after adjustment for chance agreement. If the two input images are in perfect agreement (no change has occurred), K equals 1. If the two images are completely different, K takes a value of -1. If the change between the two dates occurred by chance, then Kappa equals 0. Kappa is an index of agreement between the two input images as a whole. However, it also evaluates a per-category agreement by indicating the degree to which a particular category agrees between two dates. The per-category K can be calculated using the following formula (Rosenfield and Fitzpatrick-Lins,1986):

K = (Pii - (Pi.*P.i )/ (Pi. - Pi.*P.i )

where:

Pii = Proportion of entire image in which category i agrees for both dates

Pi. = Proportion of entire image in class i in reference image

P.i = Proportion of entire image in class i non-reference image

As a per-category agreement index, it indicates how much a category have changed between the two dates. In the evaluation, each of the two images can be used as reference and the other as non-reference.

Kappa系数是另外一种计算分类精度的方法。它是通过把所有地表真实分类中的像元总数（N）乘以混淆矩阵对角线（Xkk）的和，再减去某一类中地表真实像元总数与该类中被分类像元总数之积对所有类别求和的结果，再除以总像元数的平方差减去某一类中地表真实像元总数与该类中被分类像元总数之积对所有类别求和的结果所得到的。
    4、多分误差：指被分为用户感兴趣的类，而实际上属于另一类的像元，多分误差显示在混淆矩阵的行里面。
    5、漏分误差：指本属于地表真实分类，但没有被分类器分到相应类别中的像元数。漏分误差显示在混淆矩阵的列里。
    6、生产者（制图）精度：指假定地表真实为A类，分类器能将一幅图像的像元归为A的概率
    7、用户精度：指假定分类器将像元归到A类时，相应的地表真实类别是A的概率。