Heterogeneous Network Based Contrastive Learning Method for PolSAR Land Cover Classification

arXiv:2403.19902v1 Announce Type: new
Abstract: 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. …

abstract arxiv classification cs.cv data deep learning fields however image interpretation network performance progress quality radar supervised learning synthetic type