基于半监督空间-通道选择性卷积核网络的极化SAR图像地物分类

Terrain Classification of Polarimetric SAR Images Using Semi-supervised Spatial-channel Selective Kernel Network

针对极化合成孔径雷达(极化SAR)图像地物分类中标注样本数量少的问题,该文提出一种基于空间-通道选择性卷积核全卷积网络(SCSKFCN)和预选-联合优化半监督学习(SPUO)的极化SAR图像地物分类方法。SCSKFCN通过使用空间和通道注意力机制,对不同感受野的特征进行自适应加权融合,有效提升了模型的分类性能。SPUO能够高效地利用标注样本,挖掘无标注样本中蕴含的信息。它采用K-Wishart距离进行样本预选并生成伪标签,然后在联合优化阶段使用真实标注样本和伪标注样本同时对模型进行优化。在模型优化过程中,SPUO对伪标注样本进行两步验证并筛选可靠的伪标注样本参与优化。实验结果表明,该方法能够在只使用少量标注样本的条件下实现高精度、高效率的极化SAR图像地物分类。

In this paper,a Spatial-Channel Selective Kernel Fully Convolutional Network(SCSKFCN)and a Semi-supervised Preselection-United Optimization(SPUO)method are proposed for polarimetric Synthetic Aperture Radar(SAR)image classification.Integrated with spatial-channel attention mechanism,SCSKFCN adaptively fuses features that have different sizes of reception field,and achieves promising classification performance.SPUO can efficiently extract information contained in unlabeled samples according to annotated samples.It utilizes K-Wishart distance to preselect unlabeled samples for pseudo label generation,and then optimizes SCSKFCN with both labeled and pseudo labeled samples.During the training process of SCSKFCN,a two-step verification mechanism is applied on pseudo labeled samples to reserve reliable samples for united optimization.The experimental results show that the proposed SCSKFCN-SPUO can achieve promising performance and efficiency using limited number of annotated pixels.