DRFormer: Learning Disentangled Representation for Pan-Sharpening via Mutual Information- Based Transformer

In this article, we propose a new pan-sharpening method that disentangles low spatial resolution multispectral (LRMS) and panchromatic (PAN) images in terms of sensor-specific features and common features. These features are obtained by defining mutual information (MI)-based transformers designed to achieve disentangled learning. In the proposed method, LRMS and PAN images are cross-reconstructed by cross-coupled transformers to facilitate the disentanglement of the common features and sensor-specific features. To ensure compatibility among the disentangled features, self-reconstructions of LRMS and PAN images are imposed on them, and source images are reconstructed by self-coupled transformers. In addition to the reconstruction-guided disentangled learning, we maximize the MI between the common features of LRMS and PAN images to improve the correlation of the common features from different images. We also minimize the MI between the common features and sensor-specific features from the same image to reduce the redundancy among them. Through the reconstruction and disentangled representation of source images, sensor-specific features and common features can be decomposed efficiently. Finally, all disentangled features are integrated by a fusion transformer to generate the high spatial resolution multispectral (HRMS) image. Experiments on different datasets demonstrate that the proposed method produces competitive fusion results. The code is available at
https://github.com/RSMagneto/DRFormer
.