Dual-branch PolSAR Image Classification Based on GraphMAE and Local Feature Extraction
<|reference_start|>Dual-branch PolSAR Image Classification Based on GraphMAE and Local Feature Extraction: 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.<|reference_end|>
@article{wang2024dual-branch,
    title={Dual-branch PolSAR Image Classification Based on GraphMAE and Local
  Feature Extraction},
    author={Yuchen Wang, Ziyi Guo, Haixia Bi, Danfeng Hong and Chen Xu},
    journal={arXiv preprint arXiv:2408.04294},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.04294},
    primaryClass={cs.CV cs.LG}
}