自然资源遥感2026，Vol.38Issue(2)：22-30,9.DOI:10.6046/zrzyyg.2025029
基于改进Transformer网络的森林优势树种遥感分类
A remote sensing method for classifying dominant tree species in forests based on the modified Transformer network
摘要
Abstract
The classification and distribution mapping of tree species are indispensable to precision forestry management.However,the classification of tree species faces challenges such as the high dimensionality of remotely sensed time-series data,difficulty in feature extraction,and similar spectral features of tree species.To address these challenges,this study proposed a remote sensing method for classifying dominant tree species based on the modified Transformer network.By combining the capability of the Transformer model in capturing global features,the proposed method improved the sensitivity to the spectral-temporal features of different tree species and the identification accuracy through the optimization of time series modeling.With Ningyuan County as the study area,the dominant tree species were classified and mapped using the Sentinel-2 time series data.The results show that the TransformerToST algorithm could adaptively extract typical spectral-temporal features of key phenological stages from the satellite image time series(SITS),improving the overall accuracy and Kappa coefficient by about 5%(to 89.39%)and 0.066 0(to 0.867 2),respectively,compared to the traditional Transformer algorithm.Additionally,the cross-regional model validation in the Huangfushan forest farm confirmed the significant accuracy improvement of the modified model.The tree species map generated in this study provides data support for the dynamic monitoring,ecological conservation,and management of forest resources in the study area,as well as a technical reference for forest resource survey and assessment.关键词
树种分类/Sentinel-2/时间序列/TransformerToST/光谱-时间特征Key words
classification of tree species/Sentinel-2/time series/TransformerToST/spectral-temporal features分类
信息技术与安全科学引用本文复制引用
蔡佐茜,罗森,杨潜,魏英娟..基于改进Transformer网络的森林优势树种遥感分类[J].自然资源遥感,2026,38(2):22-30,9.基金项目
国家重点研发计划(编号:2021YFB3900505)资助. （编号:2021YFB3900505）
