Multiview Prototypical Learning and Semantic Explaining Transformer for Hyperspectral Change Detection (opens in new tab)

Abundant hyperspectral remote sensing data provides change detection (CD) technology with the opportunity to accurately distinguish land cover transformations across a consistent area over time. However, most deep learning methods for hyperspectral CD are performed in a fully supervised manner, which requires a large number of pixel-level labeled samples and is highly time-consuming. Therefore, this study combines semi-supervised learning (SSL) to reduce label acquisition costs. However, ther...