Projection-based adaptive anomaly detection for hyperspectral imagery
Adaptive anomaly detectors that find any materials whose spectral characteristics are out of context with those of the neighboring materials are proposed. We use a dual rectangular window that separates the local area into two regions- the inner window region (IWR) and outer window region (OWR). The statistical differences between the IWR and OWR is exploited by generating projection vectors onto which the IWR and OWR vectors are projected. Anomalies are detected if the projection separation between the IWR and OWR vectors is greater than a predefined threshold. Four different methods are used to produce the projection vectors. The proposed anomaly detectors have been applied to HYDICE (HYper-spectral Digital Imagery Collection Experiment) images and detection performance for each method has been measured.