用自监督学习提升热带物种分类的光谱稳定性。
Prospects for Mitigating Spectral Variability in Tropical Species Classification Using Self-Supervised Learning
- 采用巴洛-双胞胎方法提取抗环境干扰的光谱特征。
- 40种热带物种分类准确率提升10个百分点，跨日期更稳定。
- 适合遥感、生态监测领域研究者参考。
机载高光谱成像是识别热带物种的有前景方法，但不同时间采集间的光谱变异导致结果不一致。本文提出利用自监督学习（SSL）编码对非生物变异鲁棒且与物种识别相关的光谱特征。通过在重复光谱数据上应用前沿的Barlow-Twins方法，证明了可构建稳定特征。针对40种热带物种的分类实验显示，这些特征在跨日期的光谱变异鲁棒性上比典型反射率产品提升10个百分点的准确率。
原文摘要 · Abstract (English)
Airborne hyperspectral imaging is a promising method for identifying tropical species, but spectral variability between acquisitions hinders consistent results. This paper proposes using Self-Supervised Learning (SSL) to encode spectral features that are robust to abiotic variability and relevant for species identification. By employing the state-of-the-art Barlow-Twins approach on repeated spectral acquisitions, we demonstrate the ability to develop stable features. For the classification of 40 tropical species, experiments show that these features can outperform typical reflectance products in terms of robustness to spectral variability by 10 points of accuracy across dates.
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