Aircraft Target Classification Method Based on Texture Feature of Laser Echo Time-Frequency Image

Yunpeng Wang; Yihua Hu; Wuhu Lei; Liren Guo

doi:10.3788/AOS201737.1128004

Acta Optica Sinica, Volume. 37, Issue 11, 1128004(2017)

Aircraft Target Classification Method Based on Texture Feature of Laser Echo Time-Frequency Image

Yunpeng Wang, Yihua Hu^*, Wuhu Lei, and Liren Guo

Author Affiliations

State Key Laboratory of Pulsed Power Laser Technology, Electronic Engineering Institute, Hefei, Anhui 230037, China

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Figures & Tables(12)

Fig. 1. Time-frequency images of laser echo signal of three types of aircraft. (a) Helicopter; (b) propeller; (c) turbojet aircraft

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Fig. 2. Pretreatment flow of time-frequency diagram

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Fig. 3. Comparison of effect of gray scale before and after denoising. (a) Helicopter before denoising; (b) propeller before denoising; (c) turbojet aircraft before denoising; (d) helicopter after denoising; (e) propeller after denoising; (f) turbojet aircraft after denoising

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Fig. 4. GLCM of time-frequency image of three types of aircraft. (a) Helicopter 0°; (b) propeller 0°; (c) turbojet aircraft 0°; (d) helicopter 90°; (e) propeller 90°; (f) turbojet aircraft 90°

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Fig. 5. Change curves of GLCM features with different d. (a) C; (b) E; (c) H; (d) I; (e) V

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Fig. 6. Tamura feature distribution of three types of aircraft

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Fig. 7. Influence of noise on classification accuracy rate. (a) GLCM feature; (b) Tamura feature

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Fig. 8. Comparison of classification performance of two kinds of feature under different SNR conditions

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Table 1. GLCM feature parameter extraction algorithm

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Table 1. GLCM feature parameter extraction algorithm

Feature parameter	Algorithm	Range
Correlation	$\begin{matrix} G_{Cor} = \overset{G}{\sum_{i = 1}} \overset{G}{\sum_{j = 1}} [i \times j \times P (i, j, d, θ) - u_{1} \times u_{2} \frac{]}{(} d_{1} \times d_{2}) \end{matrix}$	[-1,1]
Angular second moment (ASM)	$\begin{matrix} G_{ASM} = \overset{G}{\sum_{i = 1}} \overset{G}{\sum_{j = 1}} P^{2} (i, j, d, θ) \end{matrix}$	[0,1]
Entropy	$\begin{matrix} G_{Ent} = - \overset{G}{\sum_{i = 1}} \overset{G}{\sum_{j = 1}} P (i, j, d, θ) \times lgP (i, j, d, θ) \end{matrix}$	[0,1]
Contrast	$\begin{matrix} G_{Con} = \overset{G}{\sum_{i = 1}} \overset{G}{\sum_{j = 1}} [{(i - j)}^{2} \times P (i, j, d, θ)] \end{matrix}$	[0,(G-1)²]
Homogeneity	$\begin{matrix} G_{Hom} = - \overset{G}{\sum_{i = 1}} \overset{G}{\sum_{j = 1}} P (i, j, d, θ) / [1 + {(i - j)}^{2}] \end{matrix}$	[0,1]

Table 2. GLCM feature parameter values

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Table 2. GLCM feature parameter values

Aircraft	Correlation		ASM		Entropy		Contrast		Homogeneity
Aircraft	0°	90°	0°	90°	0°	90°	0°	90°	0°	90°
Helicopter	0.0077	0.0098	0.82	0.83	0.95	0.88	57.44	12.50	0.91	0.93
Propeller	0.0026	0.0027	0.31	0.29	4.00	4.06	121.62	100.99	0.60	0.58
Turbojet aircraft	0.0118	0.0028	0.83	0.77	0.80	1.03	1.75	137.68	0.94	0.89

Table 3. Tamura feature parameter values
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Table 3. Tamura feature parameter values
Aircraft F_crs F_con F_lin
Helicopter 35.90 0.059 0.90
Propeller 22.03 0.225 0.64
Turbojet aircraft 22.75 0.073 0.87

Table 4. Simulation parameters of five turbojet aircrafts (T), eight propeller aircrafts (P) and nine helicopters (H)

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Table 4. Simulation parameters of five turbojet aircrafts (T), eight propeller aircrafts (P) and nine helicopters (H)

Category	Rotating speed /(r/min)	$\begin{matrix} {L_{1}}_{} \end{matrix}$ /m	$\begin{matrix} {L_{2}}_{} \end{matrix}$ /m	Number of blade	Category	Rotating speed /(r/min)	$\begin{matrix} {L_{1}}_{} \end{matrix}$ /m	$\begin{matrix} {L_{2}}_{} \end{matrix}$ /m	Number of blade
H-1	394.0	0	5.640	2	P-3	1150.0	0.23	1.675	4
H-2	265.0	0	7.800	4	P-4	1800.0	0.10	1.065	5
H-3	394.0	0	5.345	3	P-5	800.0	0.49	2.350	4
H-4	265.5	0	8.150	4	P-6	1380.0	0.28	1.905	6
H-5	185.0	0	10.650	5	P-7	2180.0	0.17	0.915	2
H-6	324.0	0	7.315	2	P-8	1690.0	0.23	1.180	3
H-7	205.0	0	9.450	6	T-1	3520.0	0.38	1.100	38
H-8	383.0	0	5.500	4	T-2	8615.0	0.18	0.510	27
H-9	400.0	0	4.875	3	T-3	3000.0	0.30	1.000	30
P-1	950.0	0.28	1.905	6	T-4	5000.0	0.20	0.600	33
P-2	1650.0	0.12	1.150	5	T-5	4000.0	0.24	0.800	42

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Yunpeng Wang, Yihua Hu, Wuhu Lei, Liren Guo. Aircraft Target Classification Method Based on Texture Feature of Laser Echo Time-Frequency Image[J]. Acta Optica Sinica, 2017, 37(11): 1128004

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