Abstract: In the field of multivariate alteration detection （MAD） of remote sensing images，change vector analysis in posterior probability space （CVAPS） is a widely used method. However，the CVAPS，which employs support vector machines to estimate the posterior probability vectors of remote sensing image pixels，is susceptible to various factors such as different objects with the same spectrum，the same object with different spectra，and mixed pixels in remote sensing images. These factors make it difficult to accurately estimate the magnitude and direction of the posterior probability vectors of complex pixels，consequently affecting the accuracy of multivariate alteration detection. Therefore，under the framework of CVAPS，this paper proposed a MAD method using angle thresholds，which employed the fuzzy C-means clustering to decompose mixed pixels and coupled a context-sensitive Bayesian network. When the angle is less than a certain threshold，the pixel is identified as the change type represented by the standard change vector. Experimental results show that the proposed algorithm exhibited superior alteration detection performance，achieving higher change detection accuracy than other algorithms.