The annotation of polarimetric synthetic aperture radar (PolSAR) images is a labor-intensive and time-consuming process. Therefore, classifying PolSAR images with limited labels is a challenging task in remote sensing domain. In recent years, self-supervised learning approaches have proven effective in PolSAR image classification with sparse labels. However, we observe a lack of research on generative selfsupervised learning in the studied task. Motivated by this, we propose a dual-branch classification model based on generative self-supervised learning in this paper. The first branch is a superpixel-branch, which learns superpixel-level polarimetric representations using a generative self-supervised graph masked autoencoder. To acquire finer classification results, a convolutional neural networks-based pixel-branch is further incorporated to learn pixel-level features. Classification with fused dual-branch features is finally performed to obtain the predictions. Experimental results on the benchmark Flevoland dataset demonstrate that our approach yields promising classification results.

翻译：极化合成孔径雷达（PolSAR）图像的标注是一项劳动密集且耗时的工作。因此，在遥感领域中，如何在有限标注条件下对PolSAR图像进行分类是一项具有挑战性的任务。近年来，自监督学习方法已被证明在稀疏标注的PolSAR图像分类中具有良好效果。然而，我们注意到在当前研究任务中，生成式自监督学习方面的探索尚显不足。基于此，本文提出一种基于生成式自监督学习的双分支分类模型。第一分支为超像素分支，采用生成式自监督图掩码自编码器学习超像素级别的极化表征。为获得更精细的分类结果，进一步引入基于卷积神经网络的像素分支以学习像素级特征。最终通过融合双分支特征进行分类预测。在标准Flevoland数据集上的实验结果表明，本方法能够取得优异的分类效果。