Joint cascaded 3DCNN and SDTA encoding for tree species classification using UAV-based hyperspectral image in mining areas

Reforestation is crucial for ecological restoration in mining areas, and precise classification of tree species is an important prerequisite for evaluating the effectiveness of ecological restoration. Unmanned aerial vehicles (UAVs) hyperspectral imagery enables highly accurate classification of tree species, leveraging its superior spectral and spatial resolutions. However, the challenge of dimensionality escalation, caused by hundreds of spectral features across numerous bands, introduces new hurdles for conventional classification methods. Deep learning provides a new solution for automatic feature extraction and tree classification and mapping. However, there are problems such as single scale of extracted features, static model inference, and weak model generalization ability. Especially in the ecological restoration area of the mining area, where the terrain environment is complex and tree species of multiple forest age levels coexist, and the classification accuracy needs to be improved. Therefore, this study uses UAV-based hyperspectral imagery acquired from the Jianshan mining area in Kunming, Yunnan Province, as the primary data source, and proposes a novel three-dimensional convolutional neural network (SDTA-3DCNN) incorporating a separable depth transposed attention mechanism. The model enhances the extraction of high-dimensional sparse spectral features through a cascaded 3DCNN architecture, while effectively resolving complex spectral patterns by leveraging an attention mechanism to fuse local features with high-level global representations. The final tree classification accuracy reaches F1-score = 0.9814, OA = 0.9903 and Kappa = 0.9868. The proposed method is also well applied in other tree classification and crop classification scenarios, with OA and Kappa of 0.9871 and 0.9688 in other tree classification scenarios, respectively, and the highest classification accuracy in crop classification scenarios reaches OA = 0.9712, Kappa = 0.9635. The method can provide scientific basis and technical support for the fine classification of tree species in mining areas, the monitoring of ecological restoration status in mining areas, the evaluation of effects, and the decision-making.