Progressive pseudo-label framework for unsupervised hyperspectral change detection

Abstract

For hyperspectral image change detection (HSI-CD) task, unsupervised learning methods based on pseudo-labels obtained from pre-classification method are promising due to its simplicity and generality. However, the inevitable noise and the lack of diversity in the pseudo-labels would degrade the performance of the network model, resulting in insufficient mining of fine change information in HSIs. To address this issue, we propose a progressive pseudo-label (PPL) framework for HSI-CD. Differing from the state-of-the-art methods of training designed networks with fixed pseudo-labels, we focus our attention on improving the quality of pseudo-labels progressively. In the PPL framework, a multi-scale pre-classification module is constructed for generating initial pseudo-labels. Subsequently, an uncertainty-aware selection strategy is designed to update the pseudo-labels. Specifically, the new pseudo-labels are selected based on the uncertainty estimated by the Bayesian network trained with pseudo-labels. The new pseudo-labels have richer diversity with quality assurance and thus called progressive pseudo-labels. Meanwhile, the Bayesian network trained by progressive pseudo-labels has increasingly better detection capability. As a result, the PPL framework can detect strong and subtle changes while suppressing false changes. Furthermore, the PPL framework additionally provides the uncertainty to evaluate the reliability of the prediction. We have implemented intensive experiments on three publicly available datasets demonstrate the outperformance and generalization of the PPL framework. The overall accuracy (OA) and Kappa metrics on the commonly used Hermiston dataset reached 0.9558 and 0.8728, respectively.

Keywords

• Change detection (CD)
• Hyperspectral images (HSIs)
• Uncertainty estimation
• Pseudo-label