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Learning Embedding Adaptation for ISAR Image Recognition with Few Samples

Inverse Synthetic Aperture Radar (ISAR) image recognition with few samples is a key challenge for space target recognition. Prototype network addresses this challenge by learning an embedding function from training set and applying the function to targets from test set with few samples, but the learned embedding function is not optimal relative to unknown tasks. To this end, this paper employs a type of triple attention mechanism called Transformer to transform the embeddings from task-agnostic to task-specific by establishing the dimensional dependency between labeled samples and targets to be identified in the task. The experimental results show that proposed method improves recognition accuracy by 24.92% in 5-way 1-shot task and 18.35% in 5-way 5-shot task, which verifies the effectiveness of the method.