Detection of Bark Beetle Attacks using Hyperspectral PRISMA Data and Few-Shot Learning
This work addresses forest health monitoring for environmental management, but it is incremental as it applies known few-shot learning techniques to a specific domain.
The paper tackled the problem of detecting bark beetle infestations in coniferous forests by proposing a few-shot learning approach using PRISMA hyperspectral data, achieving results that outperformed existing methods like original PRISMA bands and Sentinel-2 data.
Bark beetle infestations represent a serious challenge for maintaining the health of coniferous forests. This paper proposes a few-shot learning approach leveraging contrastive learning to detect bark beetle infestations using satellite PRISMA hyperspectral data. The methodology is based on a contrastive learning framework to pre-train a one-dimensional CNN encoder, enabling the extraction of robust feature representations from hyperspectral data. These extracted features are subsequently utilized as input to support vector regression estimators, one for each class, trained on few labeled samples to estimate the proportions of healthy, attacked by bark beetle, and dead trees for each pixel. Experiments on the area of study in the Dolomites show that our method outperforms the use of original PRISMA spectral bands and of Sentinel-2 data. The results indicate that PRISMA hyperspectral data combined with few-shot learning offers significant advantages for forest health monitoring.