用于SAR舰船分类的联合手工特征视图双流对比预测网络
Dual-stream contrastive predictive network with joint handcrafted feature view for SAR ship classification
AI总结：
本文提出双流对比预测网络DCPNet，通过非对称任务、假负样本消除和簇级任务，联合深度特征与手工特征以提升SAR舰船分类精度。
摘要：
现有的大多数合成孔径雷达（SAR）舰船分类技术严重依赖正确标注的数据，忽略了未标注SAR舰船图像的判别性特征。尽管研究者尝试通过引入传统手工特征来丰富基于CNN的特征，但现有方法容易造成信息冗余，且无法捕捉二者之间的交互关系。为解决这些问题，我们提出了一种新颖的双流对比预测网络（DCPNet），该网络由两个非对称任务设计和一个假负样本消除模块组成。第一个任务构建正样本对，引导核心编码器学习更通用的表示。第二个任务鼓励自适应地捕捉深度特征与手工特征之间的对应关系，实现模型内部的知识迁移，并有效改善特征融合带来的冗余问题。为了增大簇间的可分性，我们还设计了簇级任务。在OpenSARShip和FUSAR-Ship数据集上的实验结果表明，监督模型的分类精度得到了提升，并证实了DCPNet学习有效表示的能力。
英文摘要：
Most existing synthetic aperture radar (SAR) ship classification technologies heavily rely on correctly labeled data, ignoring the discriminative features of unlabeled SAR ship images. Even though researchers try to enrich CNN-based features by introducing traditional handcrafted features, existing methods easily cause information redundancy and fail to capture the interaction between them. To address these issues, we propose a novel dual-stream contrastive predictive network (DCPNet), which consists of two asymmetric task designs and the false negative sample elimination module. The first task is to construct positive sample pairs, guiding the core encoder to learn more general representations. The second task is to encourage adaptive capture of the correspondence between deep features and handcrated features, achieving knowledge transfer within the model, and effectively improving the redundancy caused by the feature fusion. To increase the separability between clusters, we also design a cluster-level tasks. The experimental results on OpenSARShip and FUSAR-Ship datasets demonstrate the improvement in classification accuracy of supervised models and confirm the capability of learning effective representations of DCPNet.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
