Deep Multitask Learning with Graph Constraints for Hyperspectral Images Open-Set Classification

Existing methods for hyperspectral image classification (HSIC) typically assume a closed-set scenario, where all target types are known, and the classifier can assign only predefined classes to samples (pixels). However, in real remote sensing applications, open-set scenarios are common, where unknown classes exist. To address this problem, we propose a graph-constrained deep multi-task approach for open-set HSIC. Our method tackles the challenge of detecting unknown classes by integrating multiple-class classifiers and multiple binary classifiers. Additionally, to handle the limited labeled samples issue in HSIC, we propose utilizing homogeneous and heterogeneous graphs to constrain the two types of classifiers, thereby improving the accuracy of unknown class detection and known class classification. Experimental results on the Pavia University dataset demonstrate that our proposed method outperforms other closed-set and open-set classification methods significantly.