Multitemporal change detection by spectral and multivariate texture information

Most existing multitemporal change detection methods use the spectral information alone. However, the inclusion of spatial and temporal information in change detection could improve the accuracy of change detection. This study proposes a new method which includes the multivariate texture in change detection by the direct multitemporal classification. The multivariate texture was extracted from two multispectral images, by the Pseudo Cross Multivariate Variogram (PCMV), which is an extension of the traditional Pseudo Cross Variogram (PCV). The experiments showed that the inclusion of multivariate texture could significantly improve the overall accuracy of change detection, compared to that of using the spectral information alone.