Hyperspectral Change Detection Using Semi-Supervised Graph Neural Network and Convex Deep Learning

With recent advances in hyperspectral remote sensing, hyperspectral change detection (HCD) methods have been developed for precision agriculture and land cover/use monitoring. Among the hyperspectral techniques, those based on convex optimization (CO) and deep learning (DE) have gained particular attentions. However, DE often relies on a vast amount of training data (big data) and time-consuming manual labeling tasks, in order to learn the inherent patterns of hyperspectral images (HSIs). On the other hand, CO typically requires sophisticated mathematical regularization terms for effectively and adaptively addressing the ill-posed HCD inverse problem. Considering these challenges, we employ the convex deep (CODE) small-data learning theory recently invented for hyperspectral satellite remote sensing, and propose a semi-supervised graph neural network to achieve very low labeling rates. Furthermore, the proposed CODE-HCD method exploits hyperspectral data affine geometry to design a convex Q -quadratic norm regularizer, whose mathematical form is very simple and thereby ensures a computationally efficient algorithm. The superiority of CODE-HCD over benchmark methods will be demonstrated on several real-world hyperspectral datasets.