Prototype-Based Pseudo-Label Refinement for Semi-Supervised Hyperspectral Image Classification

Pseudo-label learning-based methods usually regard class confidence above a certain threshold for unlabeled samples as pseudo-labels, which may result in pseudo-labels still containing wrong labels. In this letter, we propose a prototype-based pseudo-label refinement (PPLR) for semi-supervised hyperspectral image classification. The proposed PPLR filters wrong labels from pseudo-labels using class prototypes, which can improve the discrimination of the network. First, PPLR uses multi-head attentions to extract the spectral-spatial features, and designs an adaptive threshold that can be dynamically adjusted to generate high-confidence pseudo-labels. Then, PPLR constructs class prototypes for different categories using labeled sample features and unlabeled sample features with refined pseudo-labels to improve the quality of pseudo-labels by filtering wrong labels. Finally, PPLR further assigns reliable weights to these pseudo-labels in calculating their supervised loss, and introduces a center loss to improve the discrimination of features. When 10 labeled samples per category are utilized for training, PPLR achieves the overall accuracies of 82.11%, 86.70% and 92.50% on the Indian Pines, Houston2013 and Salinas datasets, respectively.