Purified Contrastive Learning With Global and Local Representation for Hyperspectral Image Classification

Contrastive learning has emerged as a promising technique for hyperspectral image (HSI) classification. However, the inherent limitation of sliding window sampling in HSI results in partial samples within a mini-batch exhibiting extremely high similarity. Consequently, there is an increased number of negative sample pairs composed of similar samples, significantly reducing the effectiveness of contrastive learning. Moreover, prevailing classification models heavily depends on convolutional operations, emphasizing the extraction of local features but struggle to capture long-distance dependencies in both spatial and spectral dimensions. To address these problems and fully leverage the abundance of unlabeled samples, we propose a novel purified contrastive learning (PCL) framework for HSI classification. We design a complementary spatial-spectral representation encoder architecture that combines convolutional neural network (CNN) and Transformer to capture local features and global dependencies. More importantly, a purified contrastive loss function is proposed based on super-pixel spatial prior. Extensive experiments on three public datasets demonstrate the superiority of PCL over state-of-the-art methods in HSI classification. The code for this work is available at https://github.com/zhaolin6/PCL for the sake of reproducibility.