Polarimetry-Inspired Contrastive Learning for Class-Imbalanced PolSAR Image Classification

Abstract

In recent years, deep neural networks have significantly boosted the performance of polarimetric synthetic aperture radar (PolSAR) image classification. However, existing deep learning-based approaches still suffer from the following limitations. First, the performance of them is subject to the availability of massive annotations which are difficult to acquire for PolSAR images. Secondly, the class imbalance in PolSAR data greatly hinders the correct classification of minority yet equally pivotal classes. To overcome the above shortcomings, we propose a polarimetry-inspired contrastive learning PolSAR image classification approach, in the hope of elevating the classification accuracy by taking advantage of the polarimetric domain knowledge. Firstly, a complex-valued contrastive learning framework is designed, via which powerful polarimetric representations are learnt without any manual annotations. Specifically, we innovatively design two distribution-inspired positive sample generation strategies, i.e., WishartPSG and NoisePSG, to enable discriminative and domain-specific representation learning. A novel hybrid anti-imbalance scheme is further devised to tackle the class imbalance issue, which combines a contextual consistency-based pseudo-label generation and a weighted feature-level synthetic data over-sampling technique. It should be highlighted that the domain knowledge of PolSAR, including the data and noise distributions, complex-valued characteristics and the spatial consistency prior, is fully exploited throughout our model design. Extensive experiments on four benchmark datasets demonstrated the effectiveness of the proposed model. For the Flevoland 1989 dataset, our method improves the overall accuracy, average accuracy and Kappa metrics by 3.54%, 6.81% and 7.29% respectively, compared to existing state-of-the-art method. Our code will be available at https://github.com/HaixiaBi1982/PiCL.