3 accepted papers
Currently, many change detection (CD) methods rely on supervised learning, which necessitates extensive manually annotated data, resulting in significant labor and time requirements. Recently, semi-supervised (SS) approaches have emerged in the CD community, which exploit large amounts of unlabeled…
General change detection (CD) methods require extensive annotated data to ensure effective performance, yet the annotation of remote sensing (RS) bi-temporal images is significantly time-consuming and labor-intensive. While numerous RS methods have employed semi-supervised (SS) learning to tackle th…
In recent years, deep learning has ushered in great developments in remote sensing image change detection. Practically, it is labor-intensive and time-consuming to label images for co-registration. In this paper, we propose a semi-supervised training that uses the mean teacher model to construct pse…