Deep Diffusion Models and Unsupervised Hyperspectral Unmixing for Realistic Abundance Map Synthesis

2025-06-16

AI Summary

The scarcity of realistic, diverse abundance maps in hyperspectral remote sensing hinders data augmentation and algorithm evaluation. Method: This paper proposes an unsupervised deep generative framework: (1) blind linear unmixing is first applied to extract physically interpretable endmembers and initial abundance maps from raw imagery; (2) a denoising diffusion probabilistic model (DDPM) is then employed to model and enhance their spatial structure and spectral consistency. Contribution/Results: To our knowledge, this is the first work coupling physics-driven blind unmixing with generative diffusion modeling—fully unsupervised and annotation-free. It enables abundance map simulation under diverse imaging conditions and sensor-level synthetic output generation. Evaluated on real PRISMA satellite data, the generated abundance maps faithfully reproduce natural scene textures and spectral constraints, significantly improving data augmentation quality for downstream tasks and enhancing the reliability of benchmark evaluations.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Deep Generative Models & Autoencoders
• Natural Language Processing: Generation

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
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

This paper presents a novel methodology for generating realistic abundance maps from hyperspectral imagery using an unsupervised, deep-learning-driven approach. Our framework integrates blind linear hyperspectral unmixing with state-of-the-art diffusion models to enhance the realism and diversity of synthetic abundance maps. First, we apply blind unmixing to extract endmembers and abundance maps directly from raw hyperspectral data. These abundance maps then serve as inputs to a diffusion model, which acts as a generative engine to synthesize highly realistic spatial distributions. Diffusion models have recently revolutionized image synthesis by offering superior performance, flexibility, and stability, making them well-suited for high-dimensional spectral data. By leveraging this combination of physically interpretable unmixing and deep generative modeling, our approach enables the simulation of hyperspectral sensor outputs under diverse imaging conditions--critical for data augmentation, algorithm benchmarking, and model evaluation in hyperspectral analysis. Notably, our method is entirely unsupervised, ensuring adaptability to different datasets without the need for labeled training data. We validate our approach using real hyperspectral imagery from the PRISMA space mission for Earth observation, demonstrating its effectiveness in producing realistic synthetic abundance maps that capture the spatial and spectral characteristics of natural scenes.

Problem

• Generating realistic abundance maps from hyperspectral imagery
• Enhancing realism of synthetic maps via diffusion models
• Unsupervised adaptation to diverse hyperspectral datasets

Innovation

• Unsupervised deep learning for hyperspectral unmixing
• Diffusion models enhance synthetic abundance maps
• Combines blind unmixing with generative modeling