基于像斑的变化向量分析法CVA （Change Vector Analysis）过分依赖像斑的灰度均值信息，而未能有效利用其灰度分布信息，这在高分辨率遥感影像变化检测中存在不足。本文提出了一种基于像斑直方图相似性测度的变化检测方法。利用G统计量构建不同时期像斑之间的相似性测度。假设所有像斑的相似性测度值符合混合高斯分布模型，通过期望最大化算法EM （Expectation Maximization）求解相关参数，最后采用基于最小错误率的贝叶斯判别规则获取最终的变化结果。实验表明，本文提出的上述方法能够有效提高变化检测的精度。

The object-oriented change vector analysis method, which is excessively dependent on the mean value of each object but failed to use gray distribution information, is deficient in change detection using high-resolution remote sensing images. A new method introducing similarity measurement of object histogram is proposed in this study. First, the similarity measurement of objects between different periods is built up by G statistic. Second, the Expectation Maximization (EM) algorithm is used to calculate the related parameters according to the assumption that all similarity measurement values of objects fit a Gaussian Mixture Distribution model. Finally, the Bayesian rule with minimum error rate is applied to get the change/no change results. Experimental results show that the method can get results with higher accuracy in change detection, especially for high-resolution remote sensing images.