Hyperspectral data augmentation with transformer-based diffusion models
This addresses the challenge of limited labeled data for hyperspectral image analysis, though it appears incremental as it builds on existing diffusion and transformer methods for a specific domain.
The authors tackled the problem of overfitting in deep learning for hyperspectral land-cover classification with small labeled datasets by proposing a data augmentation technique using a guided diffusion model with a lightweight transformer network, achieving superior average and weighted average accuracy compared to other methods on a forest classification task with 10 types using PRISMA satellite data.
The introduction of new generation hyperspectral satellite sensors, combined with advancements in deep learning methodologies, has significantly enhanced the ability to discriminate detailed land-cover classes at medium-large scales. However, a significant challenge in deep learning methods is the risk of overfitting when training networks with small labeled datasets. In this work, we propose a data augmentation technique that leverages a guided diffusion model. To effectively train the model with a limited number of labeled samples and to capture complex patterns in the data, we implement a lightweight transformer network. Additionally, we introduce a modified weighted loss function and an optimized cosine variance scheduler, which facilitate fast and effective training on small datasets. We evaluate the effectiveness of the proposed method on a forest classification task with 10 different forest types using hyperspectral images acquired by the PRISMA satellite. The results demonstrate that the proposed method outperforms other data augmentation techniques in both average and weighted average accuracy. The effectiveness of the method is further highlighted by the stable training behavior of the model, which addresses a common limitation in the practical application of deep generative models for data augmentation.