Forest Tree Species Classification Based on Deep Ensemble Learning by Fusing High-Resolution, Multitemporal, and Hyperspectral Multisource Remote Sensing Data

Forest tree species classification has great significance for sustainable development of forest resource. Multi-source remote sensing data provides abundant temporal, spatial, and spectral information for tree species classification. However, there lacks tree species classification methods which comprehensively capture and fuse spatio-temporal-spectral information. Therefore, a tree species classification method based on deep ensemble learning of multi-source spatio-temporal-spectral remote sensing data is proposed. Firstly, multi-temporal, high-resolution and hyperspectral data are utilized for training temporal, spatial, and spectral deep networks. Furtherly, deep ensemble learning is developed for fusion of spatio-temporal-spectral network outputs, where weighted fusion is implemented via dynamic weight optimization based on the spatio-temporal-spatial features. Experimental results indicate that the importance of temporal features is higher than that of spatial information, and spectral networks perform best among all network structures. After the spatio-temporal-spectral ensemble learning, the performance of tree species classification is further improved, and the overall accuracy of the proposed method reaches above 90%. The proposed algorithm realizes precise and fine-scale tree species classification, and provides technique support for the monitoring and conservation of forest resource.