Change detection in satellite image time series is an important domain with various applications in land study. Most previous works proposed to perform this detection by studying two images and analysing their differences. However, those methods do not exploit the whole set of images that is available today and they do not propose a description of the detected changes. We propose a sequential pattern mining approach for these image time series with two important features. First, our proposal allows for the analysis of all the images in the series and each image can be considered from multiple points of view. Second, our technique is specifically designed towards image time series where the changes are not the most frequent patterns that can be discovered. Our experiments show the relevance of our approach and the significance of our patterns.