SHSRD: Efficient Conditional Diffusion Model for Single Hyperspectral Image Superresolution
The emergence of deep neural networks has spurred progress in single hyperspectral image (HSI) superresolution. However, mainstream models prioritize network architecture and optimization, overlooking the limitations of HSI datasets’ small scale and potential data imbalance. Direct traini...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11036681/ |
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| Summary: | The emergence of deep neural networks has spurred progress in single hyperspectral image (HSI) superresolution. However, mainstream models prioritize network architecture and optimization, overlooking the limitations of HSI datasets’ small scale and potential data imbalance. Direct training models with such datasets can lead to issues, such as overfitting. To better solve the above-mentioned problems from the perspective of dataset, we propose SHSRD, an advanced superresolution framework specifically designed for HSIs based on diffusion model. It incorporates a spectral information injection module, which selectively introduces diverse spectral information into the model in a conditional manner, thereby enabling efficient spectral information perception. Furthermore, a two-stage training strategy is meticulously devised. Initially, the model undergoes pretraining on a large-scale natural image dataset. Subsequently, leveraging transfer learning techniques, the knowledge acquired from the natural image dataset is seamlessly transferred to the single HSI superresolution task. This strategic approach culminates in the realization of high-quality superresolution for single HSIs, effectively addressing the challenges associated with limited training data in hyperspectral imaging. It also demonstrates significant generalization, with only one pretraining session on a natural image dataset required for scale-specific tasks, allowing for rapid transfer to any small HSI dataset. Extensive experiments on four public datasets demonstrate that SHSRD outperforms state-of-the-art methods. |
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| ISSN: | 1939-1404 2151-1535 |