Adversarial Positive-Unlabeled Learning-Based Invasive Plant Detection in Alpine Wetland Using Jilin-1 and Sentinel-2 Imageries
Zhu E. Samat A. Li E. Xu R. Li W. Li W.
March 2025Multidisciplinary Digital Publishing Institute (MDPI)
Remote Sensing
2025#17Issue 6
Invasive plants (IPs) pose a significant threat to local ecosystems. Recent advances in remote sensing (RS) and deep learning (DL) significantly improved the accuracy of IP detection. However, mainstream DL methods often require large, high-quality labeled data, leading to resource inefficiencies. In this study, a deep learning framework called adversarial positive-unlabeled learning (APUL) was proposed to achieve high-precision IP detection using a limited number of target plant samples. APUL employs a dual-branch discriminator to constrain the class prior-free classifier, effectively harnessing information from positive-unlabeled data through the adversarial process and enhancing the accuracy of IP detection. The framework was tested on very high-resolution Jilin-1 and Sentinel-2 imagery of Bayinbuluke grasslands in Xinjiang, where the invasion of Pedicularis kansuensis has caused serious ecological and livestock damage. Results indicate that the adversarial structure can significantly improve the performance of positive-unlabeled learning (PUL) methods, and the class prior-free approach outperforms traditional PUL methods in IP detection. APUL achieved an overall accuracy of 92.2% and an F1-score of 0.80, revealing that Pedicularis kansuensis has invaded 4.43% of the local plant population in the Bayinbuluke grasslands, underscoring the urgent need for timely control measures.
adversarial learning , deep learning , invasive plants , Jilin 1 , Pedicularis kansuensis , positive-unlabeled learning
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State Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, 830011, China
University of Chinese Academy of Sciences, Beijing, 100049, China
China-Kazakhstan Joint Laboratory for Remote Sensing Technology and Application, Al-Farabi Kazakh National University, Almaty, 050012, Kazakhstan
Key Laboratory of RS & GIS Application Xinjiang, Urumqi, 830011, China
School of Geography, Geomatics and Planning, Jiangsu Normal University, Xuzhou, 221116, China
State Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands
University of Chinese Academy of Sciences
China-Kazakhstan Joint Laboratory for Remote Sensing Technology and Application
Key Laboratory of RS & GIS Application Xinjiang
School of Geography
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