Hyperspectral data augmentation with transformer-based diffusion models

Mattia Ferrari; Lorenzo Bruzzone

doi:10.1117/12.3032957

19 November 2024 Hyperspectral data augmentation with transformer-based diffusion models

Mattia Ferrari, Lorenzo Bruzzone

Proceedings Volume 13196, Artificial Intelligence and Image and Signal Processing for Remote Sensing XXX; 131960I (2024) https://doi.org/10.1117/12.3032957
Event: Remote Sensing, 2024, Edinburgh, United Kingdom

Abstract

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.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Mattia Ferrari and Lorenzo Bruzzone "Hyperspectral data augmentation with transformer-based diffusion models", Proc. SPIE 13196, Artificial Intelligence and Image and Signal Processing for Remote Sensing XXX, 131960I (19 November 2024); https://doi.org/10.1117/12.3032957

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