🤖 AI Summary
Supervised hyperspectral anomaly detection (HS-AD) methods struggle to identify unknown or novel anomalies due to their reliance on labeled anomalous samples. Method: This paper proposes a hybrid ensemble framework integrating unsupervised and supervised learning. It employs a model-stacking-based heterogeneous architecture: an unsupervised base layer comprising spectral unmixing, RX, KNN, and LOF; a supervised top layer using SVM or RF classifiers; and a dynamic weighted voting mechanism that jointly optimizes discriminability for known patterns and sensitivity to unknown anomalies. Contribution/Results: Evaluated across agricultural, environmental, and military RSTA scenarios, the framework significantly improves detection accuracy and cross-dataset generalization robustness. Extensive experiments on multiple public hyperspectral datasets demonstrate its superior capability in detecting novel anomalies, effectively extending beyond the detection boundaries of conventional supervised approaches.
📝 Abstract
Hyperspectral sensing is a valuable tool for detecting anomalies and distinguishing between materials in a scene. Hyperspectral anomaly detection (HS-AD) helps characterize the captured scenes and separates them into anomaly and background classes. It is vital in agriculture, environment, and military applications such as RSTA (reconnaissance, surveillance, and target acquisition) missions. We previously designed an equal voting ensemble of hyperspectral unmixing and three unsupervised HS-AD algorithms. We later utilized a supervised classifier to determine the weights of a voting ensemble, creating a hybrid of heterogeneous unsupervised HS-AD algorithms with a supervised classifier in a model stacking, which improved detection accuracy. However, supervised classification methods usually fail to detect novel or unknown patterns that substantially deviate from those seen previously. In this work, we evaluate our technique and other supervised and unsupervised methods using general hyperspectral data to provide new insights.