Hyperspectral Image Generation with Unmixing Guided Diffusion Model

2025-06-03

AI Summary

To address the limited diversity and difficulty in satisfying physical constraints in hyperspectral image (HSI) generation, this paper proposes a demixing-guided diffusion model (DIDM), which shifts the generative process from the high-dimensional spectral image space to the low-dimensional abundance space. The method adopts a two-module architecture: (i) a linear unmixing–based autoencoder enabling reversible RGB-to-abundance mapping while explicitly enforcing non-negativity and sum-to-one constraints in the abundance space; and (ii) an abundance diffusion module incorporating physics-based consistency regularization to enhance spectral interpretability. We introduce two novel evaluation metrics specifically designed for HSI generation. Extensive experiments demonstrate that DIDM achieves significant improvements over state-of-the-art methods in generation quality, diversity, and physical fidelity—attaining SOTA performance under both conventional and newly proposed metrics.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Deep Generative Models & Autoencoders
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Web Mining and Content Analysis: Web data generation and simulation
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

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.

Problem

• Addressing hyperspectral image diversity limitations in generative models
• Overcoming high dimensionality challenges in diffusion models for hyperspectral data
• Ensuring physical consistency in hyperspectral image generation via unmixing guidance

Innovation

• Unmixing autoencoder reduces computational complexity
• Abundance diffusion ensures physical consistency
• Novel metrics evaluate hyperspectral image quality