Unsupervised Sentinel-2 Image Fusion Using a Deep Unrolling Method

Multispectral remote-sensing images often have band-dependent image resolution due to cost and technical limitations. To address this, we developed a method that sharpens low-resolution (LR) images using high-resolution (HR) images. In this letter, we propose a novel unsupervised deep-learning (DL) approach that involves unrolling an iterative algorithm into a deep neural network and training it using a loss function based on Stein's risk unbiased estimate (SURE) to sharpen the LR bands (20 and 60 m) of Sentinel-2 (S2) to their highest resolution (10 m). This approach views traditional optimization model-based methods through a DL framework, improving interpretability and clarifying connections between the two approaches. Results from both simulated and real S2 datasets demonstrate that the proposed method outperforms competitive methods and produces high-quality sharpened images for the 20- and 60-m bands. The codes are available at https://github.com/hvn2/S2-Unrolling.