发表机构
University of Helsinki; Finnish Meteorological Institute; Xihua University; Wuhan University（赫尔辛基大学; 芬兰气象研究所; 西华大学; 武汉大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出HyUDA-One框架，结合高光谱无监督域适应与空间-光谱正则化伪正样本学习，成功绘制肯尼亚两种稀树草原中三种蜜源树种分布，提升了未标注域单类分类性能，为养蜂业提供支撑。
AI 中文摘要
养蜂业对肯尼亚农牧社区的生计多样化具有重要潜力，蜜源树种为蜜蜂提供必需的花蜜来源，但其精确空间分布信息的匮乏制约了养蜂业的全面发展。单类分类（OCC）为仅利用目标类少量标注数据检测目标物种提供了实用方案，但现有OCC方法在训练域表现良好，因域偏移导致对未见过的域泛化能力有限。为解决这些挑战，本研究提出高光谱无监督域适应单类分类框架（HyUDA-One），利用机载高光谱影像和激光雷达数据进行树种制图；设计空间-光谱正则化伪正样本学习以缓解域偏移、提升模型泛化能力。在肯尼亚南部两个稀树草原景观中对三种关键蜜源树种的制图实验验证了HyUDA-One的有效性，结果显示其在未标注域的性能显著提升：训练域中，Senegalia mellifera、Vachellia tortilis、Commiphora africana的F1值分别为0.788、0.845、0.768；未训练域中，上述两种树种的F1值分别为0.756、0.884。生成的分布图揭示了这些蜜源树种的空间格局和花蜜来源可用性，为稀树草原景观的可持续养蜂发展提供重要参考，且该框架可扩展至入侵物种检测等其他制图应用。
英文摘要
The beekeeping sector holds significant potential for livelihood diversification among the agropastoral communities in Kenya. Melliferous tree species play a critical role by providing essential nectar sources for bees. However, limited knowledge of their precise spatial distributions constrains the full development of beekeeping. One-class classification (OCC) offers a practical solution for detecting single target species without requiring extensive labeled data from other classes. Although existing OCC methods perform well in trained domains, the generalization capability to unseen domains remains limited due to domain shift. To address these challenges, this study proposes a hyperspectral unsupervised domain adaptation OCC framework (HyUDA-One) for tree species mapping using airborne hyperspectral imagery and laser scanning data. The spatial-spectral regularized pseudo-positive learning was designed to mitigate domain shift and improve model generalizability. The effectiveness of HyUDA-One was demonstrated by mapping three key melliferous tree species in two savanna landscapes in southern Kenya. The results show that HyUDA-One significantly improves performance in unlabeled domains. The F1-scores of 0.788, 0.845, and 0.768 were achieved for Senegalia mellifera, Vachellia tortilis, and Commiphora africana in the trained domain, respectively. In the untrained domain, the F1-scores of Senegalia mellifera and Vachellia tortilis were 0.756 and 0.884, respectively. The distribution maps revealed the spatial patterns of these melliferous tree species and the nectar source availability, offering an important reference for sustainable beekeeping development in savanna landscapes. Furthermore, the proposed framework can potentially be extended to other mapping applications, such as invasive species detection.
Comments18 pages. Final published version, licensed under CC BY 4.0
Journal refISPRS Journal of Photogrammetry and Remote Sensing, 232 (2026), 638-655
DOI：10.1016/j.isprsjprs.2025.12.028
