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HSIGene: A Foundation Model for Hyperspectral Image Generation.

Li Pang, Xiangyong Cao, Datao Tang

IEEE Transactions on Pattern Analysis and Machine Intelligence

|September 25, 2025

Summary

This study introduces HSIGene, a foundation model for generating hyperspectral images (HSIs). It addresses HSI scarcity and enhances generation reliability and diversity using multi-condition control and advanced data augmentation.

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Area of Science:

Remote Sensing
Computer Vision
Data Science

Background:

Hyperspectral imaging (HSI) is crucial for agriculture and environmental monitoring.
Limited HSI data due to high acquisition costs hinders downstream task performance.
Existing HSI synthesis methods struggle with reliability, diversity, and controllability.

Purpose of the Study:

To develop a novel HSI generation foundation model, HSIGene, addressing limitations of current methods.
To enable precise and reliable HSI generation with multi-condition control.
To improve spatial diversity and spectral fidelity in synthesized HSIs.

Main Methods:

Proposed HSIGene, a latent diffusion model with multi-condition control for HSI generation.
Introduced a data augmentation method using spatial super-resolution to increase training data diversity.
Developed a two-stage HSI super-resolution framework, including a Rectangular Guided Attention Network (RGAN).

Main Results:

HSIGene demonstrated the capability to generate a large volume of realistic HSIs.
The proposed data augmentation and super-resolution methods enhanced spatial diversity while preserving spectral fidelity.
Generated HSIs proved effective for downstream tasks like denoising and super-resolution.

Conclusions:

HSIGene offers a significant advancement in controllable and reliable HSI synthesis.
The integrated data augmentation and super-resolution techniques effectively address HSI data scarcity.
The model provides a valuable resource for advancing HSI applications in various scientific fields.