用少量标注数据+高光谱卫星图，精准识别松树虫害
Detection of Bark Beetle Attacks using Hyperspectral PRISMA Data and Few-Shot Learning
- 用对比学习预训练1D-CNN，从高光谱数据提取稳定特征
- 仅用少量标签样本，就能准确估算每像素健康/受害/死亡树木比例
- 在意大利多洛米蒂山区表现优于原始波段和哨兵2号数据
松材线虫侵害严重威胁针叶林健康。本文提出一种基于对比学习的少样本学习方法，利用卫星PRISMA高光谱数据检测松树虫害。方法采用对比学习框架预训练一维CNN编码器，从高光谱数据中提取鲁棒特征表示；这些特征作为输入，用于为每个类别训练支持向量回归估计算器，基于少量标注样本估算每个像素中健康、受虫害及死亡树木的比例。在多洛米蒂山区的实验表明，该方法优于使用原始PRISMA光谱波段及哨兵2号（Sentinel-2）数据的表现。结果表明，结合PRISMA高光谱数据与少样本学习，在森林健康监测中具有显著优势。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。