Target Classification of Hyperspectral Lidar Based on Optimization Selection of Spatial-Spectral Features

Bowen Chen; Shuo Shi; Wei Gong; Qian Xu; Xingtao Tang; Sifu Bi; Biwu Chen

doi:10.3788/AOS221717

Acta Optica Sinica, Volume. 43, Issue 12, 1228008(2023)

Target Classification of Hyperspectral Lidar Based on Optimization Selection of Spatial-Spectral Features

Bowen Chen^1,2,3, Shuo Shi^2,3,4、*, Wei Gong^2,3,4, Qian Xu², Xingtao Tang², Sifu Bi², and Biwu Chen⁵

Author Affiliations

¹Chinese Antarctic Center of Surveying and Mapping, Wuhan University, Wuhan 430079, Hubei, China

²State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, Hubei, China

³Electronic Information School, Wuhan University, Wuhan 430079, Hubei, China

⁴Collaborative Innovation Center of Geospatial Technology, Wuhan 430079, Hubei, China

⁵Shanghai Radio Equipment Research Institute, Shanghai 201109, China

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Figures & Tables(12)

Fig. 1. System prototype of hyperspectral lidar

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Fig. 2. Scanning scene of 14 different targets

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Fig. 3. Target classification processes based on hyperspectral lidar for spatial-spectral feature optimization selection

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Fig. 4. True color reconstruction result based on optimal band combination

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Fig. 5. Real categories of fourteen targets

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Fig. 6. Target classification results of the first four classification strategies. (a) 32-channel spectral information and elevation value; (b) spectral indices; (c) geometric features; (d) spatial-spectral feature combination

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Fig. 7. Target classification results of the 5th and 6th classification strategies. (a) Spatial-spectral features selected by marine predator algorithm; (b) optimal spatial-spectral feature combination

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Fig. 8. Correlation of spatial-spectral features

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Table 1. Vegetation indices used in this study

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Table 1. Vegetation indices used in this study

Vegetation index	Formula	Reference
Differential vegetation index（DVI）	$R_{775} - R_{675}$	［25］
Ratio vegetation index（RVI）	$\frac{R_{775}}{R_{675}}$	［26］
Enhanced vegetation index（EVI）	$\frac{2.5 (R_{775} - R_{675})}{1 + R_{775} + 2.4 R_{675}}$	［27］
Soil adjusted vegetation index（SAVI）	$\frac{1.5 (R_{775} - R_{675})}{R_{775} + R_{675} + 0.5}$	［28］
Normalized differential vegetation index（NDVI）	$\frac{R_{775} - R_{675}}{R_{775} + R_{675}}$	［29］
Ratio normalized differential vegetation index（RNDVI）	$\frac{R_{775}^{2} - R_{675}}{R_{775} + R_{675}^{2}}$	［30］
Red-edge chlorophyll index（CIred-edge）	$\frac{R_{745}}{R_{705}} - 1$	［31］
Modified chlorophyll absorption ratio index（MCARI）	$\frac{R_{695}}{R_{665}} - 0.2 (R_{695} - R_{495}) \frac{R_{695}}{R_{665}}$	［32］
Plant senescence reflectance index（PSRI）	$\frac{R_{675} - R_{495}}{R_{745}}$	［33］
Triangular vegetation index（TVI）	$0.5 [120 (R_{745} - R_{495}) - 200 (R_{665} - R_{545})]$	［34］

Table 2. Color indices used in this study

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Table 2. Color indices used in this study

Color index	Formula	Reference
Excess green index（ExG）	$2 G - R - B$	［38］
Normalized green‑red difference index（NGRDI）	$\frac{G - R}{G + R}$	［39］
Normalized green‑blue difference index（NGBDI）	$\frac{G - B}{G + B}$	［40］
Excess red index（ExR）	$1.4 R - G$	［41］
Excess green minus excess red（ExGR）	$E_{x} G - E_{x} R$	［42］
Visible atmospherically resistant index（VARI）	$\frac{G - R}{G + R - B}$	［43］
Visible‑band diference vegetation index（VDVI）	$\frac{2 G - R - B}{2 G + R + B}$	［44］
Modified green red vegetation index（MGRVI）	$\frac{G^{2} - R^{2}}{G^{2} + R^{2}}$	［45］
Red green blue vegetation index（RGBVI）	$\frac{G^{2} - R B}{G^{2} + R B}$	［45］
Normalized redness intensity（NRI）	$\frac{R}{G + R + B}$	［46］
Green minus red difference index（GMR DI）	$G - R$	［47］

Table 3. Six different target classification strategies

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Table 3. Six different target classification strategies

Target classification strategy	Classification feature
1	Original spectral information and elevation
2	Spectral index features
3	Spatial features
4	Original spectral information，elevation，spectral index features，and spatial features
5	Spatial‑spectral features selected by marine predator algorithm
6	Optimal spatial-spectral feature combination

Table 4. Classification accuracy summary

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Table 4. Classification accuracy summary

Strategy	1		2		3		4		5		6
OA /%	91.49		90.73		89.56		95.57		96.66		97.13
AA /%	77.74		78.27		68.26		84.37		87.44		89.05
Kappa	0.8934		0.8837		0.8693		0.9380		0.9577		0.9642
Time /s	4.72（±0.32）		4.18（±0.45）		3.53（±0.22）		5.16（±0.47）		4.06（±0.18）		3.61（±0.17）
Class	P	R	P	R	P	R	P	R	P	R	P	R
1	0.98	0.96	0.98	0.95	1.00	0.99	1.00	1.00	1.00	1.00	1.00	1.00
2	0.90	0.91	0.88	0.87	0.84	0.75	0.92	0.92	0.92	0.97	0.93	0.96
3	0.82	0.79	0.75	0.80	0.68	0.76	0.83	0.88	0.89	0.91	0.91	0.92
4	0.93	0.91	0.93	0.91	0.88	0.85	0.97	0.93	0.98	0.93	0.99	0.95
5	0.81	0.88	0.84	0.88	0.92	0.93	0.91	0.93	0.94	0.95	0.91	0.97
6	0.93	0.88	0.77	0.83	0.87	0.79	0.96	0.93	0.99	0.97	0.99	0.99
7	0.88	0.86	0.90	0.84	0.73	0.70	0.95	0.91	0.97	0.96	0.98	0.95
8	0.73	0.77	0.77	0.76	0.36	0.59	0.79	0.80	0.83	0.82	0.88	0.91
9	0.90	0.93	0.90	0.93	0.87	0.92	0.94	0.96	0.94	0.97	0.97	0.97
10	0.69	0.71	0.57	0.65	0.67	0.67	0.82	0.94	0.88	0.94	0.91	0.99
11	0.67	0.90	0.77	0.81	0.33	0.51	0.86	0.88	0.86	0.89	0.89	0.86
12	0.93	0.87	0.90	0.91	0.91	0.90	0.96	0.95	0.97	0.95	0.98	0.94
13	0.33	1.00	0.40	0.75	0.20	0.33	0.40	0.86	0.40	0.86	0.47	0.88
14	0.40	0.84	0.62	0.82	0.29	0.44	0.65	0.85	0.67	0.92	0.65	0.92

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Bowen Chen, Shuo Shi, Wei Gong, Qian Xu, Xingtao Tang, Sifu Bi, Biwu Chen. Target Classification of Hyperspectral Lidar Based on Optimization Selection of Spatial-Spectral Features[J]. Acta Optica Sinica, 2023, 43(12): 1228008

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Paper Information

Category: Remote Sensing and Sensors

Received: Sep. 19, 2022

Accepted: Nov. 29, 2022

Published Online: Jun. 20, 2023

The Author Email: Shi Shuo (shishuo@whu.edu.cn)

DOI:10.3788/AOS221717

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology