Noise-to-Abundance Translation: Unsupervised Hyperspectral Unmixing Based on Diffusion Models

Hyperspectral unmixing is an important issue in remote sensing field for identifying the constituent materials and their respective distributions in a scene. In recent years, a large number of data-driven HSI unmixing methods have been proposed. These methods reformulate the unmixing task as a transformation from spectral pixels to abundance. Diffusion models, a recently emerging class of generative models, have shown potential in converting noise into other meaningful representations. However, applying diffusion models to unsupervised hyperspectral unmixing faces the following challenges: stochasticity of the generative process, neglect of the quality and prior knowledge of the estimation of original data, and difficulty in solving the learning optimization problem. To address these issues, we propose a novel diffusion-based unsupervised hyperspectral unmixing framework, enabling the transformation from noise to abundance maps. First, we embed the diffusion process into an autoencoder architecture and introduce a denoising guidance generator to extract robust guidance from hyperspectral images, directing the reverse diffusion process to produce spatially aligned and physically plausible abundances. Second, we design an abundance evaluation module that supervises the original data, i.e., abundances in this context, using decoder-based reconstruction evaluation and unmixing prior knowledge. Additionally, we adopt a stage-wise freezing training strategy to progressively stabilize the model and address the learning optimization problem. Experimental results on both simulated and real-world datasets demonstrate that the proposed method effectively addresses the challenges of unsupervised hyperspectral unmixing, producing accurate, physically meaningful, and robust unmixing results.