J. Eur. Opt. Society-Rapid Publ. 21, 31( 2025) 13
( a)( b)( c)( d)( e)
( f)( g)( h)( i)( j)
Fig. 11. ROC curve of detection results. Before dimensionality reduction:( a) SMF.( b) ACE.( c) HUD.( d) RGAE.( e) Auto-AD. After dimensionality reduction:( f) SMF.( g) ACE.( h) HUD.( i) RGAE.( j) Auto-AD.
Table 2. The running time of dimensionality reduction.
Dataset |
Size |
Median filtering |
SSDC |
Per-Pixel method |
Hyres |
Gulfport |
100 100 191 |
0.4139 |
0.5709 |
4.7181 |
0.9542 |
HYDICE |
80 100 175 |
0.4257 |
0.4492 |
3.5177 |
0.8782 |
Urban |
100 100 204 |
0.4485 |
0.6219 |
5.6646 |
1.1343 |
San Diego-1 |
120 120 189 |
0.5559 |
0.7594 |
6.2395 |
0.9781 |
San Diego-2 |
100 100 189 |
0.4211 |
0.5694 |
4.7431 |
0.9062 |
Cement Street |
500 300 89 |
3.3312 |
3.9968 |
17.1578 |
4.5059 |
Holly |
496 600 89 |
4.4476 |
6.8643 |
35.5684 |
8.3849 |
Jungle |
1000 1000 89 |
15.2333 |
24.7317 |
147.9942 |
26.8665 |
Average |
– |
3.1597 |
4.8205 |
28.2004 |
5.576 |
Fig. 12. Relationship between runtime and data volume.( a) Median filtering.( b) SSDC.( c) MR method.( d) Hyres.
GLCM Correlation, and only outperforms LRCRP in PSNR. The possible reason is that MNF only focuses on improving the signal-to-noise ratio, while the dimensionality reduction mechanisms of other algorithms contain limitations on the overall pixel variation. This indicates that the spatial information preservation term added by the proposed method has a good effect on preserving spatial information.
3.5 The impact on the accuracy of target detection
This section discusses the performance of the proposed method and comparative dimensionality reduction methods in target detection. The comparative methods and target detection methods are selected as mentioned above. Figures 13 – 20 illustrate the detection performance of various dimensionality reduction methods across eight