A novel method integrating sample migration and threshold optimization for high-precision greenhouse classification: evidence from Southern China
Luo K. Zhang H. Zhu C. Jiao T. Samat A. Chen Y. Cheng C.
2025Taylor and Francis Ltd.
Geocarto International
2025#40Issue 1
Greenhouses are vital for food security and agricultural modernization, yet their classification in remote sensing imagery is challenging due to scattered distribution, small scale, and spectral similarities. This study proposes a remote sensing classification framework using sample migration and dynamic threshold optimization for accurate, scalable greenhouse detection. Historical samples are migrated to the target area based on spectral similarity, reducing reliance on new labeled datasets and improving cross-regional generalization. A post-classification threshold optimization module corrects misclassifications using multi-dimensional indices, enhancing robustness in complex spectral environments. Empirical validation across six provinces in southern China showed superior performance (96.48% OA, 94.36% Kappa), outperforming traditional methods. This framework enables precise mapping of small-scale greenhouses in heterogeneous regions, supporting agricultural monitoring. It aids government agencies in crop area estimation, land-use tracking, and precision agriculture, while private enterprises benefit for asset evaluation and planning, promoting sustainable resource management and addressing large-scale land cover classification challenges.
agricultural monitoring , Greenhouses , remote sensing classification , sample migration , threshold optimization
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College of Geography and Environmental Engineering, Gannan Normal University, Ganzhou, China
State Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, China
School of Geospatial Information, Information Engineering University, Zhengzhou, China
College of Geography and Remote Sensing Science, Xinjiang University, Urumqi, China
China-Kazakhstan Joint Laboratory for Remote Sensing Technology and Application, Al-Farabi Kazakh National University, Almaty, Kazakhstan
College of Geography and Environmental Engineering
State Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands
School of Geospatial Information
College of Geography and Remote Sensing Science
China-Kazakhstan Joint Laboratory for Remote Sensing Technology and Application
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