本文提出了一种面向对象的多特征融合的变化检测方法。首先通过影像分割获取像斑，然后统计各像斑的光谱直方图和LBP(local binary patterns)纹理直方图，利用G统计量计算不同时期像斑之间的光谱距离和纹理距离，采用自适应的方法将光谱距离和纹理距离加权构建像斑的异质性，最后结合EM(expectation maximization)算法和贝叶斯最小错误率理论获取像斑的变化类别。在QuickBird影像上的实验表明该方法能够充分融合光谱特征和纹理特征，从而提高变化检测的精度。

In order to make full use of spectral and texture features, an object-oriented change detection method for remote sensing images based on multi-features fusion is proposed in this paper. First image segmentation is used to get image objects. Then the spectral and lbp texture histograms of each object are extracted. G statistic is adopted to calculate the distance of histograms between two periods. The heterogeneity of each object is built by weighted spectral and texture distance. At last, the expectation maximization algorithm and bayesian rule with minimum error rate are applied to get the change/no change results. Experimental results on QuickBird and SPOT-5 images show that the method proposed in this article can integrate the spectral and texture features effectively and improves the accuracy of change detection.