Adaptability Analysis of Multiple Features Detection Algorithms Based on Fusion Degree Model Between Target and Environment

Xian-ming DENG; Tian-cai ZHANG; Zeng-can LIU; Zhong-sheng LI; Jie XIONG; Yi-xiang ZHANG; Peng-hao LIU; Yi CEN; Fa-lin WU

doi:10.3964/j.issn.1000-0593(2022)04-1285-08

Journals >Spectroscopy and Spectral Analysis >Volume 42 >Issue 4 >Page 1285 > Article

Spectroscopy and Spectral Analysis
Vol. 42, Issue 4, 1285 (2022)

Adaptability Analysis of Multiple Features Detection Algorithms Based on Fusion Degree Model Between Target and Environment

Xian-ming DENG^1、1;, Tian-cai ZHANG^1、*, Zeng-can LIU^1、1;, Zhong-sheng LI^1、1;, Jie XIONG^1、1;, Yi-xiang ZHANG^1、1;, Peng-hao LIU^1、1;, Yi CEN^{2、2; *;}, and Fa-lin WU^1、1;

Author Affiliations

¹1. The 59th Research Institute of China Ordnance Industry, Chongqing 400039, China

²2. Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

show less

DOI: 10.3964/j.issn.1000-0593(2022)04-1285-08 Cite this Article

Xian-ming DENG, Tian-cai ZHANG, Zeng-can LIU, Zhong-sheng LI, Jie XIONG, Yi-xiang ZHANG, Peng-hao LIU, Yi CEN, Fa-lin WU. Adaptability Analysis of Multiple Features Detection Algorithms Based on Fusion Degree Model Between Target and Environment[J]. Spectroscopy and Spectral Analysis, 2022, 42(4): 1285 Copy Citation Text

show less

Test scenarios(a): Grassland background and vegetation camouflage target; (b): Soil background and vegetation camouflage target; (c): Soil background and vegetation and cement road camouflage targets; (d): Grassland/cement road/soil background and its corresponding camouflage targets

Fig. 1. Test scenarios
(a): Grassland background and vegetation camouflage target; (b): Soil background and vegetation camouflage target; (c): Soil background and vegetation and cement road camouflage targets; (d): Grassland/cement road/soil background and its corresponding camouflage targets

Download full size | View in the Article

Detection results of vegetation camouflage targets under grassland background using different algorithms(a): MtACE; (b): MtAMF; (c): MtCEM; (d): SumACE; (e): SumAMF; (f): SumCEM; (g): WtaACE; (h): WtaAMF; (i): WtaCEM

Fig. 2. Detection results of vegetation camouflage targets under grassland background using different algorithms
(a): MtACE; (b): MtAMF; (c): MtCEM; (d): SumACE; (e): SumAMF; (f): SumCEM; (g): WtaACE; (h): WtaAMF; (i): WtaCEM

Download full size | View in the Article

Fig. 3. Detection results of vegetation camouflage targets under soil background using different algorithms
(a): MtACE; (b): MtAMF; (c): MtCEM; (d): SumACE; (e): SumAMF; (f): SumCEM; (g): WtaACE; (h): WtaAMF; (i): WtaCEM

Download full size | View in the Article

Fig. 4. Detection results of vegetation and soil camouflage targets under soil background using different algorithms
(a): MtACE; (b): MtAMF; (c): MtCEM; (d): SumACE; (e): SumAMF; (f): SumCEM; (g): WtaACE; (h): WtaAMF; (i): WtaCEM

Download full size | View in the Article

Fig. 5. Detection results of vegetation/cement road/soil camouflage targets under grassland/cement road/soil background using different algorithms
(a): MtACE; (b): MtAMF; (c): MtCEM; (d): SumACE; (e): SumAMF; (f): SumCEM; (g): WtaACE; (h): WtaAMF; (i): WtaCEM

Download full size | View in the Article

Fig. 6. Detection results of four scenarios by nine multiple features detection algorithms

Download full size | View in the Article

Fig. 7. Detection results of three SNR scenarios by nine multiple features detection algorithms

Download full size | View in the Article

Number	Test Schemes	Similarity between targets	Similarity between target and background
1	Detection of vegetation camouflage targets under grassland background	large	large
2	Detection of vegetation camouflage targets under soil background	large	small
3	Detection of vegetation and road camouflage targets under soil background	small	small
4	Detection of vegetation, soil, road camouflage targets under grassland, soil, road background	small	large

Table 1. Test scheme

View in the Article

Number	Algorithms	FD	Accuracy
1	MtACE	0.315 3	1.0
2	MtAMF	0.394 9	0.921 8
3	MtCEM	0.736 1	0.865 5
4	SumACE	0.391 5	0.931 1
5	SumAMF	0.391 5	0.931 1
6	SumCEM	0.858 8	0.864 2
7	WtaACE	0.666 8	0.683 6
8	WtaAMF	0.666 8	0.683 6
9	WtaCEM	0.937 4	0.642 1

Table 2. FD and accuracy parameter of test 1

View in the Article

Number	Algorithms	FD	Accuracy
1	MtACE	0.318 6	1.0
2	MtAMF	0.372 3	0.944 2
3	MtCEM	0.878 8	0.689 0
4	SumACE	0.366 6	0.956 4
5	SumAMF	0.366 6	0.956 4
6	SumCEM	0.861 1	0.864 0
7	WtaACE	0.744 5	0.753 5
8	WtaAMF	0.744 5	0.753 5
9	WtaCEM	0.964 3	0.593 5

Table 3. FD and accuracy parameter of test 2

View in the Article

Number	Algorithms	FD	Accuracy
1	MtACE	0.353 6	1.0
2	MtAMF	0.382 1	0.905 8
3	MtCEM	0.878 1	1.726 9
4	SumACE	0.430 2	0.940 2
5	SumAMF	0.430 2	0.940 2
6	SumCEM	0.914 7	0.805 5
7	WtaACE	0.691 6	0.712 5
8	WtaAMF	0.691 6	0.712 5
9	WtaCEM	0.986 8	0.457 3

Table 4. FD and accuracy parameter of test 3

View in the Article

Number	Algorithms	FD	Accuracy
1	MtACE	0.292 4	1.0
2	MtAMF	0.251 6	0.920 4
3	MtCEM	0.956 4	0.640 1
4	SumACE	0.275 0	0.956 2
5	SumAMF	0.275 0	0.956 2
6	SumCEM	0.749 8	0.936 3
7	WtaACE	0.616 9	0.632 5
8	WtaAMF	0.616 9	0.632 5
9	WtaCEM	0.904 3	0.796 8

Table 5. FD and accuracy parameter of test 4

View in the Article

Number	Algorithms	SNR=200	SNR=200	SNR=400	SNR=400	SNR=800	SNR=800
Number	Algorithms	FD	Accuracy	FD	Accuracy	FD	Accuracy
1	MtACE	0.381 2	0.754 3	0.305 1	0.821 1	0.254 3	0.873 8
2	MtAMF	0.394 5	0.860 1	0.306 4	0.886 6	0.261 7	0.922 2
3	MtCEM	0.773 3	0.821 5	0.748 2	0.839 0	0.802 7	0.874 3
4	SumACE	0.449 6	0.842 4	0.361 2	0.873 0	0.297 9	0.917 9
5	SumAMF	0.449 6	0.842 4	0.361 2	0.873 0	0.297 9	0.917 9
6	SumCEM	0.841 8	0.791 0	0.843 6	0.813 3	0.857 2	0.857 7
7	WtaACE	0.691 5	0.701 1	0.695 9	0.705 8	0.699 5	0.699 8
8	WtaAMF	0.691 5	0.701 1	0.695 9	0.705 8	0.699 5	0.699 8
9	WtaCEM	0.956 6	0.574 1	0.962 9	0.552 7	0.961 4	0.562 4

Table 6. Detection results under different signal-to-noise ratios

View in the Article

Algorithms	Tests of spectrum		Tests of noise
Algorithms	Mean	Variance	Mean	Variance
MtACE	0.320 0	0.021 9	0.313 5	0.052 1
MtAMF	0.350 2	0.057 5	0.320 9	0.055 2
MtCEM	0.862 4	0.079 5	0.774 7	0.022 3
SumACE	0.365 8	0.057 1	0.369 6	0.062 2
SumAMF	0.365 8	0.057 1	0.369 6	0.062 2
SumCEM	0.846 1	0.059 9	0.847 5	0.006 9
WtaACE	0.680 0	0.046 0	0.695 6	0.003 3
WtaAMF	0.680 0	0.046 0	0.695 6	0.003 3
WtaCEM	0.948 2	0.030 8	0.960 3	0.002 7

Table 7. Statistics of detection mean and standard deviation of multi-feature detection algorithms in different scenarios

Download Citation

Save the article for my favorites

Paper Information