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Received：11 July 2023，
Published：07 July 2024
移动端阅览
合成孔径雷达（SAR）舰船检测是近年来的研究热点。然而，与光学图像不同，SAR成像的特点会导致不直观的特征表示。此外，由于SAR图像数据量不足，现有的基于大量标记SAR图像的方法可能难以达到较好的检测效果。为了解决这些问题，本文提出了一种基于多级跨模态对齐的SAR图像舰船检测算法MCMA-Net（Multi-level Cross-Modality Alignment Network），通过将光学模态中丰富的知识迁移到SAR模态来增强SAR图像的特征表示。该算法首先设计了一个基于邻域—全局注意力的特征交互网络NGAN（Neighborhood-Global Attention Network），通过对骨干网络的浅层特征采用邻域注意力机制进行局部交互、对深层特征采取全局自注意力机制进行全局上下文交互，在兼顾全局上下文建模能力的同时，提升局部特征的编码能力，使得网络在不同层级更合理的关注相应的信息，从而能够促进后续的多级别模态对齐。其次，本文设计了一个多级模态对齐模块MLMA（Multi-level Modality Alignment），通过从局部级别到全局级别再到实例级别的对两种模态不同隐含空间中的特征进行对齐，促进模型有效地学习模态不变特征，缓解了光学图像和SAR图像之间的模态鸿沟，实现了从光学模态到SAR模态的知识传输。大量的实验证明我们的算法优于现阶段的检测算法，取得了最好的实验结果。
In recent years
interest in Synthetic Aperture Radar (SAR) ship detection has considerably grown. Its distinctive strengths position it as a pivotal player in numerous fields of research. However
the inherent characteristics of SAR images have presented a range of challenges. For instance
in contrast to optical images
SAR images have counterintuitive feature representation. Additionally
owing to the constrained number of SAR image data
achieving satisfactory results with existing methods that depend on a substantial number of annotated SAR images might be challenging.
How to effectively train a high-performance SAR ship detection network with a limited quantity of SAR images should be investigated. Given that single-modality SAR detection algorithms have inherent limitations
other effective modalities that can assist the SAR modality in completing tasks are needed. For instance
in SAR image target detection
optical images can serve as supplementary data sources. A knowledge-rich model can be developed by utilizing a large volume of optical data in training with SAR data. Hence
reasonable training approaches for effectively utilizing images from SAR and optical modalities should be explored.
To address these challenges
a SAR ship detection algorithm called MCMA-Net
which is based on multilevel cross-modality alignment
is proposed in this paper. The MCMA-Net enriches SAR feature representation by incorporating valuable knowledge from optical modality. First
we propose a neighborhood–global attention-based feature interaction network (NGAN)
which employs a neighborhood attention mechanism that enables the local interaction of low-level features and a global self-attention mechanism that captures global context from high-level features. When the ability of global context modeling is considered
the encoding ability of local features improves
NGAN enables the network to focus on corresponding information at different levels and can promote the subsequent multilevel modality alignment. Second
we propose a multilevel modality alignment module (MLMA)
which aligns features in the different hidden spaces of the two modalities from three levels. MLMA facilitates the model to acquire modality-invariant features
bridging the modality gap and realizing optical knowledge transmission. Valuable information from the optical modality can compensate for certain deficiencies in SAR images. With the aid of these two modules
we have incorporated optical superiority information by leveraging SAR’s inherent advantages
achieving an enhancement in the performance of SAR detection tasks.
Our algorithm is superior to current detection algorithms. Notably
whether on public SAR image datasets or our own SAR image dataset
the MCMA-Net consistently achieves optimal detection results
which indicates the model’s stable performance and robustness. The visualization results indicate that the MCMA-Net achieves excellent detection capabilities in complex scenarios. The ablation experiments demonstrate that compared with the baseline model
our algorithm achieved a 2.7% increase in mAP on the SSDD dataset. Various experimental results have consistently validated the rationality of the MCMA-Net.
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