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Unmixing Before Fusion: A Generalized Paradigm for Multi-Source-based Hyperspectral Image Synthesis
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Proposes "unmixing before fusion", which unmixes unpaired HSI and RGB data into shared endmembers and abundances, then synthesizes abundance maps with generative models (VDVAE, StyleGAN3, DDPM) and recombines them with HSI endmembers to produce synthetic hyperspectral images.
Problem
Hyperspectral image data is scarce and costly to acquire, and existing HSI synthesis methods (physical modeling, affine augmentation, spectral super-resolution) lack authenticity, diversity, or the ability to generate new samples; generating high-dimensional HSIs directly with generative models is d
Approach
Assume multi-source data of similar scenes share endmembers; unmix HSI and unpaired RGB images with a linear mixing model, generate samples in the low-dimensional abundance space by fusing HSI and RGB abundances, then reconstruct HSIs as endmembers times synthetic abundances.
Key results
- UBF+StyleGAN3 achieves FID 7.69 with recall 0.556 and precision 0.502, versus 39.13 FID for UBF+VDVAE.
- Abundance-based DDPM with 5 endmembers reaches FID 8.23, while generating directly in the 128-band HSI cube remains noise after 5 million steps.
- On HSRS-SC, AlexNet accuracy rises from 89.51% (no augmentation) to 94.29% with 8k UBF-augmented samples; VGG-16 rises from 87.30% to 94.60%.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use StyleGAN3 rather than VDVAE for abundance synthesis under UBF | Unmixing before Fusion · Synthetic HSIs | FID: 39.13 -> 7.69 | Helped |
| use DDPM rather than VDVAE for abundance synthesis under UBF | Unmixing before Fusion · Synthetic HSIs | FID: 39.13 -> 8.23 | Helped |
| augment training with UBF-generated HSIs instead of affine transformations | HSRS-SC scene classification · HSRS-SC | overall scene classification accuracy: 91.11% -> 92.70%; 88.84% -> 93.97%; 41.75% -> 44.33% | Helped |
| increase UBF-augmented training set from 4k to 8k | HSRS-SC scene classification · HSRS-SC | overall scene classification accuracy: 92.70% -> 94.29%; 93.97% -> 94.60%; 44.33% -> 45.76% | Helped |
| replace abundance-space generation with original HSI-cube-space generation (ablation) | DDPM synthesis · Chikusei HSI | generation quality | Hurt |
| replace abundance-space generation with latent-feature-space generation (ablation) | DDPM synthesis · Chikusei HSI | generation quality | Hurt |
| generate abundances with 15 rather than 5 endmembers (ablation) | Abundance-based DDPM · Chikusei HSI synthesis | FID: 8.23 -> 49.15 | Hurt |
| generate abundances with 8 rather than 5 endmembers (ablation) | Abundance-based DDPM · Chikusei HSI synthesis | FID: 8.23 -> 15.19 | Hurt |
| generate abundances with 3 rather than 5 endmembers (ablation) | Abundance-based DDPM · Chikusei HSI synthesis | FID: 8.23 -> 8.77 | Hurt |
| generate abundances with 15 rather than 5 endmembers (ablation) | Abundance-based DDPM · Chikusei HSI synthesis | Recall: 0.50 -> 0.11 | Hurt |
| generate abundances with 8 rather than 5 endmembers (ablation) | Abundance-based DDPM · Chikusei HSI synthesis | Recall: 0.50 -> 0.36 | Hurt |
| generate abundances with 3 rather than 5 endmembers (ablation) | Abundance-based DDPM · Chikusei HSI synthesis | Precision: 0.48 -> 0.41 | Hurt |
Surprising findings
- Generating directly in the high-dimensional HSI cube space with DDPM yields meaningless noise even after 5 million training iterations.
- Five endmembers gave the best trade-off between generation quality and computational cost, outperforming 8 and 15 endmembers in FID.
Limitations
- Generative model choice is not novel; the paradigm relies on the assumption that multi-source data of similar scenes share endmembers.
- Downstream evaluation is limited to scene classification on four classes of one small HSI dataset (HSRS-SC).
- Evaluation of synthesis quality relies on FID and precision/recall without a reference; diversity assessment is subjective.
Experimental setup
Datasets: Chikusei AID HSRS-SC
Benchmarks: HSRS-SC
Metrics: FID Precision Recall RMSE PSNR SSIM SAD scene classification accuracy
Baselines: GMM MST++ affine transformation augmentation symmetric autoencoder
Builds on: linear mixing model VDVAE StyleGAN3 DDPM MST++
Closest papers by meaning
- Unmixing Diffusion for Self-Supervised Hyperspectral Image Denoising (CVPR 2024 · similarity 0.90)
- EigenSR: Eigenimage-Bridged Pre-Trained RGB Learners for Single Hyperspectral Image Super-Resolution (AAAI 2025 · similarity 0.86)
- Enhancing Hyperspectral Images via Diffusion Model and Group-Autoencoder Super-resolution Network (AAAI 2024 · similarity 0.86)
- HIR-Diff: Unsupervised Hyperspectral Image Restoration Via Improved Diffusion Models (CVPR 2024 · similarity 0.86)
- S2CycleDiff: Spatial-Spectral-Bilateral Cycle-Diffusion Framework for Hyperspectral Image Super-resolution (AAAI 2024 · similarity 0.85)
- OTIAS: OcTree Implicit Adaptive Sampling for Multispectral and Hyperspectral Image Fusion (AAAI 2025 · similarity 0.85)
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