An Unsupervised Transformer-based Multivariate Alteration Detection Approach for Change Detection in VHR Remote Sensing Images

Multi-temporal 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 paper, 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 (C 2 VA) and the Iteratively Reweighted Multivariate Alteration Detection (IR-MAD) to generate reliable pseudo-training 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 bi-temporal satellite remote sensing datasets, and the obtained experimental results demonstrated its superiority comparing with the state-of-the-art unsupervised CD methods.