Class-wise Graph Embedding-Based Active Learning for Hyperspectral Image Classification

Deep learning (DL) techniques have shown remarkable progress in remotely sensed hyperspectral image (HSI) classification tasks. The performance of DL-based models highly relies on the quality and quantity of labeled data. However, manual labeling is a laborious and expensive process that requires substantial efforts from human experts. Active learning (AL) techniques have been developed to alleviate the burden of manual annotation by selecting the most informative and uncertain samples for labeling. In this paper, we propose a new class-wise graph embedding-based AL (CGE-AL) framework implemented by a class-wise graph convolutional network (CGCN). First, we train a classifier with labeled data and infer latent features from labeled and unlabeled samples with the trained parameter. Then, we group the labeled data into multiple one-label sets by category. In a class-wise manner, we initialize the nodes of the graph with one-label and unlabeled features, which are then fed into CGCN. By updating the graph parameters with binary loss, CGCNs measure the uncertainty between labeled nodes and unlabeled nodes. To select the most valuable sample for labeling, we adopt the class minimum uncertainty to query the unlabeled nodes with higher overall uncertainty. We repeat this process with the updated labeled set to retrain our classification model and CGCNs. Extensive experiments demonstrate the outstanding performance of our method compared to other state-of-the-art AL-based approaches.