A spectral anomaly detection method includes the steps of segmenting a panchromatic image, obtained from a hyperspectral sensor into cluster data sets. Principal component analysis can be separately performed on each of the cluster data sets to produce a plot of principal components. An anomaly detection algorithm can be applied to an adaptively selected subset of principal components for each of the cluster data sets to produce cluster detection scores. Finally, separate detection thresholding algorithms can be applied to each of the cluster detection scores, and the results of the detection thresholding algorithms can be combined into a single detection plane.
Spectral anomaly detection in deep shadows