The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Voi. XXXVII. Part B7. Beiji j 2 QOS 
Figure 7. Causal RX (a), ACAD (b), 
Figure (5) shows the visual results of global weighted RX-base local CFT (c), global CFT (d) 
methods. 
Mob*I l*X ‘■>‘>0* W**<B*4 «Wft? 
Figure 5. Weighted RX (a), weighted RX-UTD (b), 
Weighted modified RX (c), weighted normalized RX (d) 
Figure (6) shows the visual results of local RX-base algorithms. 
Figure 6. Local RX (a), local modified RX (b), 
local normalized RX (c), local RX-UTD (d) 
For comparing all implemented algorithms together, their area 
under ROC curves, number of correct detections (from 70 target 
pixels) and false alarm detections in 99% confidence level 
displayed in table (1). 
Method 
AUC 
Correct 
Detected 
False Alarm 
1 
Global NRX 
0.6429 
11 
201 
2 
Global UTD 
0.8237 
29 
189 
3 
Local NRX 
0.8981 
47 
143 
4 
Causal RX 
0.9248 
39 
141 
5 
Global RX-UTD 
0.9413 
46 
133 
6 
ACAD 
0.9464 
54 
33 
7 
Local DWEST 
0.9569 
59 
106 
8 
Local MRX 
0.9625 
55 
99 
9 
Global MRX 
0.9641 
49 
132 
10 
Local CFT 
0.9666 
58 
52 
11 
Global RX 
0.9686 
53 
108 
12 
Local UTD 
0.9698 
59 
113 
13 
Global DWEST-RX 
0.9706 
60 
14 
14 
Global DWEST 
0.9719 
63 
110 
15 
NSWTD 
0.9731 
64 
89 
16 
Local RX 
0.9764 
59 
67 
17 
Local RX-UTD 
0.9801 
64 
86 
18 
Global CFT 
0.9823 
60 
41 
19 
Global Weighted MRX 
0.9861 
60 
89 
20 
Global Weighted UTD 
0.9934 
60 
81 
21 
Global Weighted RX 
0.9944 
61 
71 
22 
Global Weighted RX UTD 
0.9946 
61 
68 
Table 1. Comparative result of anomaly detection algorithms 
Moreover, all of these algorithms are compared with 
computational complexity point of view and they are tested by 
simulated hyperspectral data with various additive Gaussian 
noises (20:1, 10:1 and 5:1 signal-to-noise ratio) to investigate 
noise sensitivity of them. For example figure (8) shows the 
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