Deformable-based multi-scale self-attentive feature fusion for SAR image ship recognition

ZHOU Hui, ZHU Hong, CHEN Peng

doi:10.16411/j.cnki.issn1006-7736.2024.04.012

Journal of Dalian Maritime University >

2024 , Vol. 50 >Issue 4: 110 - 118

DOI: https://doi.org/10.16411/j.cnki.issn1006-7736.2024.04.012

Deformable-based multi-scale self-attentive feature fusion for SAR image ship recognition

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(1.School of Computer and Software, Dalian Neusoft Information University, Dalian 116023, China;2.Navigation College, Dalian Maritime University, Dalian 116026, China)

Online published: 2024-06-05

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Abstract

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 recognition model DCN-MSF-TR, which drawed on the idea of Transformer encoder-decoder and added a deformable convolutional 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 utilize 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.

Key words： synthetic apeture radar(SAR) images; ship recognition; transformer architecture; deformable convolution; feature fusion

Cite this article

ZHOU Hui, ZHU Hong, CHEN Peng . Deformable-based multi-scale self-attentive feature fusion for SAR image ship recognition[J]. Journal of Dalian Maritime University, 2024 , 50(4) : 110 -118 . DOI: 10.16411/j.cnki.issn1006-7736.2024.04.012

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