## 3 Results
### Detection Performance
Moreover, the detection probability of SSCRD is always higher than that of CRD and CRBORAD when the false-alarm rate changes from 0 to 1, showing a considerable improvement compared to CRD. The RCO curves of LRSRD and SSCRD are slightly different, and the AUC value (in percent) of SSCRD and LRSRD are 94.77 and 92.90, respectively, indicating the superior detection performance of SSCRD for the San Diego-I image. Figure 2: _Cont._  
For the San Diego-II image, the false color image, ground-truth maps, the result of over-segmentation, the label map \(P\) obtained from the selective search, and the corresponding color detection graphs are shown in Figure 4. The threshold \(T\) in the region fusion is set at 1.40 for the San Diego-II image because of the optimal regional fusion effect. To present the intuitive and effective improvement of SSCRD compared with CRD, the sizes of the dual-window for the San Diego-II image are set at (9,13) for CRD, CRBORAD, and SSCRD, respectively. Figure 3: ROC curves for San Diego-I dataset with dual-window sizes (7,11) for CRD, CRBORAD, and SSCRD. Figure 2: Color detection maps for San Diego-I image with dual-window sizes (7,11). (**a**) False color image; (**b**) ground-truth map; (**c**) over-segmentation; (**d**) label map \(P\); (**e**) the detection result of CRD; (**f**) the detection result of CRBORAD; (**g**) the detection result of LRSRD; (**h**) the detection result of SSCRD. which is a typical sample of unbefitting dual-window sizes for CRD in the San Diego-II image. As shown in Figure 4e, the anomaly detection effectiveness of CRD is very general. First, the shapes of the two airplanes as the abnormal target to be identified at the lower left corner are relatively obscure, and it is difficult to distinguish them from the background pixels misjudged as abnormal. Second, the position and shape of the other airplane as the abnormal target to be identified are completely indistinguishable. Compared with CRD, the anomaly detection capacity of CRBORAD improves to some extent, as shown in Figure 4f. Not only are the positions and shape of the two airplanes at the lower left corner generally discernible, but also some anomaly pixels belonging to another smaller airplane also stand out. However, the shape of this anomaly target is still too fuzzy to recognize it as an airplane. Due to the selective search, both the positions and shapes of the three aircraft are clearly presented in the color detection graph of SSCRD. The scores of those anomaly pixels are high enough to highlight them from a complex background, thereby achieving effective anomaly detection. Thus, SSCRD greatly improves the hyperspectral anomaly detection capability of CRD. As for LRSRD, the two airplanes in the lower left corner are clearly detected, but the other airplane is vague and unrecognizable. Many background pixels in the upper-right and lower-right corners of the image possess high scores, which disrupts the effective detection of anomalies. The ROC curves of the four anomaly detectors under the dual-window sizes of (9,13) for the San Diego-II image are shown in Figure 5 for a quantitative comparison of detection performance. As shown in Figure 5, the probability of detection for CRD is less than 0.6 when the false alarm rate is 0.1, while the probability of detection for CRBORAD is about 0.78, and that for SSCRD is already close to 1. The furthest away from the upper left corner of the coordinate axis indicates that the anomaly detection accuracy of CRD needs to improve. The ROC curve of SSCRD is much closer to the upper left corner of the coordinate system, indicating a great improvement compared with CRD and a better detection performance than that of CRBORAD and LRSRD. The corresponding AUC values (in percent) are 85.19, 88.69, 94.30, and 98.68 for CRD, CRBORAD, LRSRD, and SSCRD, respectively.