  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.
