Robust Metric based Anomaly Detection in Kernel Feature Space
Other literature type
This thesis analyzes the anomalous measurement metric in high dimension feature
space, where it is supposed the Gaussian assumption for state-of-art mahanlanobis algorithms is
reasonable. The realization of the detector in high dimension feature space is by kernel trick.
Besides, the masking and swamping effect is further inhibited by an iterative approach in the
feature space. The proposed robust metric based anomaly detection presents promising
performance in hyperspectral remote sensing images: the separability between anomalies and
background is enlarged; background statistics is more concentrated, and immune to the
contamination by anomalies.