Mapping melliferous tree species in Kenya via one-class classification with hyperspectral unsupervised domain adaptation
2026-08-03 • Computer Vision and Pattern Recognition
Computer Vision and Pattern Recognition
AI summaryⓘ
The authors studied how to find important nectar-producing trees in Kenya using special aerial images and laser data. They developed a new method called HyUDA-One that can identify tree species in areas without much labeled data, even when the environment changes. Their approach helped accurately map three key tree species used by beekeepers, which is useful for improving beekeeping in savanna regions. This method might also help detect other types of plants, like invasive species.
One-class classificationhyperspectral imagerydomain adaptationmelliferous treesbeekeepingsavanna landscapeSenegalia melliferaVachellia tortilisCommiphora africanalaser scanning data
Authors
Zhaozhi Luo, Janne Heiskanen, Ilja Vuorinne, Ian Ocholla, Shiqi Zhang, Saana Järvinen, Xinyu Wang, Yanfei Zhong, Petri Pellikka
Abstract
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.