Deformable-based multi-scale self-attentive feature fusion for SAR image ship recognition
The feature differentiation of different categories of ship targets in SAR images is not clear,and the recognition accuracy may decrease when there are many ship categories.To better extract category features,this paper proposed a rec-ognition model DCN-MSF-TR,which drawed on the idea of Transformer encoder-decoder and added a deformable convolu-tional module(DCN)to the backbone network.At the same time,the feature layers processed by Transformer multi-scale self attention were fused at appropriate positions in the model in a feature pyramid manner,and each layer can not only uti-lize its own information,but also comprehensively utilize the features of other layers.The validation results on the Open SARShip-3 Complex three class dataset and Open SARShip-6 Complex six class dataset show that the average recognition accuracy reaches 78.1%and 66.7%,respectively,which show that the proposed method can more effectively identify ship categories in SAR images compared to other recognition models.
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