Multiview fusion algorithms have been widely applied in synthetic aperture radar automatic target recognition (SAR ATR). However, they usually ignore semantic information, which results in limited recognition performance. Therefore, how to effectively integrate semantic information into multiview fusion algorithms and explore the correlation between multiple views and semantic information is a crucial and urgent problem. To address this problem, we develop a semantics-assisted multiview fusion (SMVF) algorithm, which includes three loss terms, that is, the view-specific loss term, the semantics-regularized loss term, and the view-semantics-coupled loss term. To be specific, the view-specific and semantics-regularized loss terms convert multiple views and semantic information into their corresponding sparse codes, respectively. The view-semantics-coupled loss term constrains the sparse codes of multiple views and semantic information to explore their correlation. Finally, these three loss terms are jointly optimized to acquire the optimal sparse codes to calculate the reconstruction errors for recognition, by which SMVF not only effectively exploits semantic information, but also explores the correlation between multiple views and semantic information. Extensive experiments demonstrate that SMVF achieves high recognition accuracies (i.e., 99.7%, 91.6%, and 97.2%) under three scenarios (i.e., EOC-1, EOC-2, and SAR-ACD), which are better than other advanced algorithms.

中文翻译：

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语义辅助多视图融合SAR自动目标识别

多视图融合算法在合成孔径雷达自动目标识别（SAR ATR）中得到了广泛的应用。然而，它们通常忽略语义信息，这导致识别性能有限。因此，如何有效地将语义信息融入到多视图融合算法中，探索多视图与语义信息之间的相关性是一个至关重要而紧迫的问题。为了解决这个问题，我们开发了一种语义辅助多视图融合（SMVF）算法，其中包括三个损失项，即视图特定损失项、语义正则化损失项和视图语义耦合损失项。具体来说，视图特定损失项和语义正则化损失项分别将多个视图和语义信息转换为其相应的稀疏代码。视图语义耦合损失项约束多个视图和语义信息的稀疏代码以探索它们的相关性。最后，联合优化这三个损失项，得到最优的稀疏码，计算识别的重构误差，SMVF不仅有效地利用了语义信息，而且探索了多个视图与语义信息之间的相关性。大量实验表明，SMVF在EOC-1、EOC-2和SAR-ACD三种场景下均取得了较高的识别准确率（分别为99.7%、91.6%和97.2%），优于其他先进算法。