ExplainS2A: Explainable Spectral-Spatial Duality Model for Fast Transforming Sentinel-2 Image to AVIRIS-Level Hyperspectral Image

Mainstream optical satellites (e.g., ESA's Sentinel-2) often acquire multispectral multiresolution images, which have limited material identifiability compared to the hyperspectral images (HSIs). Thus, spectrally super-resolving the multispectral image (MSI) into their hyperspectral counterparts greatly facilitates remote material identification and the downstream tasks. However, spectrally super-resolving the MSI into an HSI is often constrained by the multiresolution nature of the sensor (e.g., Sentinel-2). Specifically, due to the presence of some low-resolution (LR) bands in the MSI, the initial spectral super-resolution results often appear to be spatially blurry, resulting in an LR HSI. To overcome this bottleneck, we then leverage some high-resolution (HR) band inherent in the acquired MSI (e.g., panchromatic band) to spatially guide the reconstruction procedure, thereby yielding the desired HR HSI. This fusion procedure elegantly coincides with a widely known spatial super-resolution problem in satellite remote sensing. Hence, we have reformulated the tough spectral super-resolution problem into a more widely investigated spatial super-resolution problem, referred to as the spectral-spatial duality theory. Accordingly, we propose ExplainS2A, consisting of a deep unfolding network and an explainable fusion network, that unifies spectral recovery and spatial fusion into a single explainable framework. Unlike conventional black-box models, ExplainS2A offers interpretability and operates as a linear-time algorithm. Remarkably, it can process a million-scale Sentinel-2 image in less than one second, yielding high-fidelity HSI over the same scene, and upgrades the blind source separation (BSS) results. Although demonstrated on the Sentinel-2 and AVIRIS sensors, ExplainS2A also serves as a general framework applicable to various sensor pairs with different resolution configurations, and has experimentally demonstrated cross-region and cross-season generalization ability. Source codes: https://github.com/IHCLab/ExplainS2A.