Task-aware Few-shot SAR Image Classification Method Based on Multi-scale Attention Mechanism
Aiming at the problem of the lack of labeled samples in SAR image classification,this paper proposes a task-aware few-shot SAR image classification method based on multi-scale attention mechanism.In order to fully mine local features and focus on the key local semantic patches under specific tasks,this paper introduces two effective attention mechanisms to obtain more effi-cient and rich feature representation.First,in the feature extraction stage,the complemented squeeze-and-excitation attention block(CSE Block)is used to focus on the salient features of different semantic parts of the original features.It can extract secon-dary salient features from the suppressed features and merge them with the main salient features,which can obtain more efficient and rich feature representation.Subsequently,an adaptive episodic attention block(AEA Block)is used to obtain key semantic patches in the entire task,which can enhance the differentiated information between tasks and improve the accuracy of SAR image classification tasks.The results show that the classification accuracy of the 5-way 1-shot task is 2.9％higher than that of the sub-optimal task on the SAR image classification standard MSTAR dataset.In the two tasks,the runtime of the proposed method is the same as other metric-learning methods,without additional excessive computing resources,which verifies its effectiveness.
万方数据
维普
