DEAE: Diffusion-Enhanced Autoencoder Network for Unsupervised Nonlinear Hyperspectral Unmixing

Hyperspectral unmixing (HU) aims to decompose mixed pixels into their constituent spectral signatures and estimate their corresponding fractional abundances. Recently, the nonlinear spectral mixing model (NLMM) has advanced significantly and offered strong physical interpretability. However, the effective integration of physics-driven NLMM with data-driven deep learning (DL) approaches still remains a critical challenge. To address this, we propose a diffusion-enhanced autoencoder (DEAE), a novel unsupervised framework that innovatively incorporates the diffusion model (DM) into nonlinear HU. DEAE introduces the residual second-order attention mechanism to capture global spectral information, adaptively weighting informative bands while compressing redundant bands. Subsequently, we integrate the extended multilinear mixing model (EMLM) into the DM-enhanced decoder, which extracts latent features from the linear autoencoder’s output and generates an enhanced reconstructed image while simultaneously estimating the transition probabilities of EMLM. Finally, a nonlinear decoder outputs the ultimate reconstructed image based on both the enhanced reconstructed image and transition probabilities. Experiments conducted on synthetic and three real-world datasets demonstrate the superior performance of DEAE compared to the state-of-the-art methods based on both LMM and NLMM.