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Multitemporal change detection (CD) plays a crucial role in the remote sensing application field. In recent years, supervised deep learning methods have shown excellent performance in detecting changes in very-high-resolution (VHR) images. However, these methods require a large number of labeled samples for training, making the process time-consuming and labor-intensive. Unsupervised approaches are more attractive in practical applications since they can produce a CD map without relying on any ground reference or prior knowledge. In this article, we propose a novel unsupervised CD approach, named transformer-based multivariate alteration detection (trans-MAD). It utilizes a pre-detection strategy that combines the compressed change vector analysis and the iteratively reweighted multivariate alteration detection (IR-MAD) to generate reliable pseudotraining samples. More accurate and robust CD results can be achieved by leveraging the IR-MAD to detect insignificant changes and by incorporating the transformer-based attention mechanism to model the difference or similarity between two distant pixels in an image. The proposed trans-MAD approach was validated on two VHR bitemporal satellite remote sensing datasets, and the obtained experimental results demonstrated its superiority comparing with the state-of-the-art unsupervised CD methods.