Hyperspectral Image Generation with Unmixing Guided Diffusion Model
Abstract
Recently, hyperspectral image generation has received increasing attention, but existing generative models rely on conditional generation schemes, which limits the diversity of generated images. Diffusion models are popular for their ability to generate high-quality samples, but adapting these models from RGB to hyperspectral data presents the challenge of high dimensionality and physical constraints. To address these challenges, we propose a novel diffusion model guided by hyperspectral unmixing. Our model comprises two key modules: an unmixing autoencoder module and an abundance diffusion module. The unmixing autoencoder module leverages unmixing guidance to shift the generative task from the image space to the low-dimensional abundance space, significantly reducing computational complexity while preserving high fidelity. The abundance diffusion module generates samples that satisfy the constraints of non-negativity and unity, ensuring the physical consistency of the reconstructed HSIs. Additionally, we introduce two evaluation metrics tailored to hyperspectral data. Empirical results, evaluated using both traditional metrics and our proposed metrics, indicate that our model is capable of generating high-quality and diverse hyperspectral images, offering an advancement in hyperspectral data generation.
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