Spectral-Cascaded Diffusion Model for Remote Sensing Image Spectral Super-Resolution

Hyperspectral remote sensing images (HSIs) have unique advantages in urban planning, precision agriculture, and ecology monitoring since they provide rich spectral information. However, hyperspectral imaging usually suffers from low spatial resolution and high cost, which limits the wide application of hyperspectral data. Spectral super-resolution provides a promising solution to acquire hyperspectral images with high spatial resolution and low cost, taking RGB images as input. Existing spectral super-resolution methods utilize neural networks following a single-shot framework, i.e., final results are obtained by one-stage spectral super-resolution, which struggles to capture and model the complex relationships between spectral bands. In this article, we propose a spectral-cascaded diffusion model (SCDM), a coarse-to-fine spectral super-resolution method based on the diffusion model. The diffusion model fits the real data distribution through stepwise denoising, which is naturally suitable for modeling rich spectral information. We cascade the diffusion model in the spectral dimension to gradually refine the spectral trends and enrich spectral information of the pixels. The cascade solves the highly ill-posed problem of spectral super-resolution step-by-step, mitigating the inaccuracies of previous single-shot approaches. To better utilize the potential of the diffusion model for spectral super-resolution, we design image condition mixture guidance (ICMG) to enhance the guidance of image conditions and progressive dynamic truncation (PDT) to limit cumulative errors in the sampling process. Experimental results demonstrate that our method achieves state-of-the-art performance in spectral super-resolution. Codes can be found at https://github.com/Mr-Bamboo/SCDM.