基于因果注意的遥感情景下细粒度船舶分类对比学习网络

Remote. Sens. Pub Date : 2023-07-03 DOI:10.3390/rs15133393
Chaofan Pan, Runsheng Li, Q. Hu, C. Niu, Wei Liu, Wanjie Lu
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引用次数: 0

摘要

舰船目标的细粒度分类是遥感领域的一项重要任务,在军事侦察和海上监视中有着广泛的应用。由于各种成像因素的影响,遥感图像中的船舶目标具有相当大的类间相似性和类内差异性,这给细粒度分类带来了很大的挑战。为此,我们开发了一个基于因果注意的对比学习网络(C2Net),以提高模型从局部细节进行细粒度识别的能力。采用“解耦+聚合”的异步特征学习模式,减少局部特征之间的相互影响,提高局部特征的质量。在解耦阶段,利用解耦函数对舰船目标各部分的特征向量进行去相关处理,防止特征粘附。考虑到结果和特征之间存在错误关联的可能性,在反事实因果注意网络的基础上设计解耦部分,增强模型的预测逻辑。在聚合阶段,利用解耦阶段学习到的局部关注权值对主干特征权值进行特征融合。然后,利用所提出的特征重关联模块对融合特征中包含的目标局部信息进行重关联和整合,得到目标特征向量;最后利用聚合函数完成目标特征向量的聚类过程,实现细粒度分类。在两个大规模数据集上的实验结果表明,C2Net方法比其他方法具有更好的细粒度分类效果。
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Contrastive Learning Network Based on Causal Attention for Fine-Grained Ship Classification in Remote Sensing Scenarios
Fine-grained classification of ship targets is an important task in remote sensing, having numerous applications in military reconnaissance and sea surveillance. Due to the influence of various imaging factors, ship targets in remote sensing images have considerable inter-class similarity and intra-class difference, which brings significant challenges to fine-grained classification. In response, we developed a contrastive learning network based on causal attention (C2Net) to improve the model’s fine-grained identification ability from local details. The asynchronous feature learning mode of “decoupling + aggregation” is adopted to reduce the mutual influence between local features and improve the quality of local features. In the decoupling stage, the feature vectors of each part of the ship targets are de-correlated using a decoupling function to prevent feature adhesion. Considering the possibility of false associations between results and features, the decoupled part is designed based on the counterfactual causal attention network to enhance the model’s predictive logic. In the aggregation stage, the local attention weight learned in the decoupling stage is used to carry out feature fusion on the trunk feature weight. Then, the proposed feature re-association module is used to re-associate and integrate the target local information contained in the fusion feature to obtain the target feature vector. Finally, the aggregation function is used to complete the clustering process of the target feature vectors and fine-grained classification is realized. Using two large-scale datasets, the experimental results show that the proposed C2Net method had better fine-grained classification than other methods.
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