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This study examines normalizing the imagery and the optimization metrics to enhance anomaly and change detection,
respectively. The RX algorithm, the standard anomaly detector for hyperspectral imagery, more successfully extracts
bright rather than dark man-made objects when applied to visible hyperspectral imagery. However, normalizing the
imagery prior to applying the anomaly detector can help detect some of the problematic dark objects, but can also miss
some bright objects. This study jointly fuses images of RX applied to normalized and unnormalized imagery and has a
single decision surface. The technique was tested using imagery of commercial vehicles in urban environment gathered
by a hyperspectral visible/near IR sensor mounted in an airborne platform. Combining detections first requires
converting the detector output to a target probability. The observed anomaly detections were fitted with a linear
combination of chi square distributions and these weights were used to help compute the target probability. Receiver
Operator Characteristic (ROC) quantitatively assessed the target detection performance. The target detection
performance is highly variable depending on the relative number of candidate bright and dark targets and false alarms
and controlled in this study by using vegetation and street line masks. The joint Boolean OR and AND operations also
generate variable performance depending on the scene. The joint SUM operation provides a reasonable compromise
between OR and AND operations and has good target detection performance. In addition, new transforms based on
normalizing correlation coefficient and least squares generate new transforms related to canonical correlation analysis
(CCA) and a normalized image regression (NIR). Transforms based on CCA and NIR performed better than the standard
approaches. Only RX detection of the unnormalized of the difference imagery in change detection provides adequate
change detection performance.
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