A Semiparametric Model for Hyperspectral Anomaly Detection
Figure 16
Detection algorithms’ output surfaces for Cube 1 (far left). The intensity of local peaks reflects the strength of evidences as seen by different anomaly detection approaches. Boundary issues were ignored in this test; surfaces were magnified to about the size of the original image only for the purpose of visual comparison. FLD, RX, KRX, and performed local anomaly detection by testing spectra within a testing window (red square shown in the scene display—far left, top) to spectra surrounding the testing window (outer window bounded by yellow lines). QG-SemiP performed global anomaly detection, as presented in this paper.
