Accurate wetland classification methods can quickly grasp the spatial-temporal variation characteristics of wetlands and play an important role in wetland research.Considering the limitation of the existing wetland classification method based on few-shot learning to the use of target or single-source domain dataset,this paper proposes a 3D multisource domain self-attention few-shot learning(3D-MDAFSL)model.First,combining the advantages of convolution and attention mechanism,a 3D feature extractor based on self-attention mechanism and deep residual convolution is designed.Then,the conditional adversarial domain adaptation strategy is used to achieve multisource domain distribution alignment,and few-shot learning is performed separately in each domain.Finally,the features extracted by the trained model are imputed to the K-nearest neighbor classifier to obtain classification results.Results show that compared with the framework without feature extraction,the 3D feature extractor improves the overall accuracy by approximately 6.79％.When using multisource domain datasets,the overall accuracy of the 3D-MDAFSL model for the Sentinel-2 wetland dataset in Zhongshan City can reach 93.52％,which is a significant improvement compared with the existing algorithms.The 3D-MDAFSL model proposed in this paper has good application value in the high-precision extraction and classification of wetland features.
维普
万方数据