Heterogeneous Network Based Contrastive Learning Method for PolSAR Land Cover Classification
<|reference_start|>Heterogeneous Network Based Contrastive Learning Method for PolSAR Land Cover Classification: Polarimetric synthetic aperture radar (PolSAR) image interpretation is widely used in various fields. Recently, deep learning has made significant progress in PolSAR image classification. Supervised learning (SL) requires a large amount of labeled PolSAR data with high quality to achieve better performance, however, manually labeled data is insufficient. This causes the SL to fail into overfitting and degrades its generalization performance. Furthermore, the scattering confusion problem is also a significant challenge that attracts more attention. To solve these problems, this article proposes a Heterogeneous Network based Contrastive Learning method(HCLNet). It aims to learn high-level representation from unlabeled PolSAR data for few-shot classification according to multi-features and superpixels. Beyond the conventional CL, HCLNet introduces the heterogeneous architecture for the first time to utilize heterogeneous PolSAR features better. And it develops two easy-to-use plugins to narrow the domain gap between optics and PolSAR, including feature filter and superpixel-based instance discrimination, which the former is used to enhance the complementarity of multi-features, and the latter is used to increase the diversity of negative samples. Experiments demonstrate the superiority of HCLNet on three widely used PolSAR benchmark datasets compared with state-of-the-art methods. Ablation studies also verify the importance of each component. Besides, this work has implications for how to efficiently utilize the multi-features of PolSAR data to learn better high-level representation in CL and how to construct networks suitable for PolSAR data better.<|reference_end|>
@article{cai2024heterogeneous,
    title={Heterogeneous Network Based Contrastive Learning Method for PolSAR Land
  Cover Classification},
    author={Jianfeng Cai, Yue Ma, Zhixi Feng, Shuyuan Yang},
    journal={arXiv preprint arXiv:2403.19902},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.19902},
    primaryClass={cs.CV}
}