Intelligent Evaluation Method of Grottoes Surface Weathering Based on Multispectral Imaging and Random Forest Algorithm

Chipeng Cao; Huiqin Wang; Ke Wang; Zhan Wang; Gang Zhang; Tao Ma

doi:10.3788/AOS202040.2230001

Acta Optica Sinica, Volume. 40, Issue 22, 2230001(2020)

Intelligent Evaluation Method of Grottoes Surface Weathering Based on Multispectral Imaging and Random Forest Algorithm

Chipeng Cao¹, Huiqin Wang^1、*, Ke Wang¹, Zhan Wang², Gang Zhang², and Tao Ma²

¹School of Information and Control Engineering, Xi'an University of Architecture and Technology, Xi'an, Shaanxi 710055, China

²Shanxi Provincial Institute of Cultural Relics Protection, Xi'an, Shaanxi 710075, China

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Figures & Tables(13)

Fig. 1. Characterization of reflection spectrum of grottoes surface with different weathering types and degrees

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Fig. 2. Spectral reflectance of grotto surface with different weathering types and degrees

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Fig. 3. Characteristics of the first derivative of spectral reflectance of grotto surface with different weathering types and degrees

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Fig. 4. Technical block diagram of weathering types and degrees evaluation method

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Fig. 5. Experimental process

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Fig. 6. Multispectral imaging data collection on grotto surface. (a) RGB image of target collection area; (b) 640 nm multi-spectral image of target area; (c) distribution of sampling points

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Fig. 7. Multi-spectral data reconstruction flow chart

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Fig. 8. Normalized spectral reflectance data of grotto surface with different weathering types and degrees

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Fig. 9. Characteristics of the first derivative of standardized spectral reflectance date of grotto surface with different weathering types and degrees

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Fig. 10. Assessment results of pure weathering area

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Fig. 11. Assessment results of four algorithms on overall weathering types and degrees of grotto surface. (a) True weathering types and degrees; (b) RF algorithm; (c) SVM algorithm; (d) SAM algorithm; (e) CNN algorithm

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Table 1. Comparison of prediction results of four algorithms
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Table 1. Comparison of prediction results of four algorithms
Algorithm Train accuracy /% Test accuracy /% Prediction accuracy /% Kappa coefficient
RF 99.91 99.89 98.49 0.98
SVM 97.63 94.53 90.28 0.86
SAM 98.72 97.78 62.65 0.51
CNN 99.99 99.64 59.61 0.57

Table 2. Comparison of confusion matrix of four algorithms

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Table 2. Comparison of confusion matrix of four algorithms

Algorithm	Class	Classification ratio /%				Total /%
Algorithm	Class	W_strong	W_slight	W_dust	W_weak	Total /%
	W_strong	99.92	0	0	0	14.05
	W_slight	0	100	0.11	0.21	10.05
RF	W_dust	0	0	99.89	0	53.05
	W_weak	0.08	0	0	99.79	22.86
	Total	100	100	100	100	100
	W_strong	89.04	0	0	4.31	15.78
	W_slight	4.28	97.32	4.38	16.97	8.35
SVM	W_dust	0	0.53	94.11	0.57	52.55
	W_weak	6.68	2.16	1.50	78.15	23.32
	Total	100	100	100	100	100
	W_strong	80.13	0.05	0	47.45	28.81
	W_slight	0.01	95.68	0.06	0	25.29
SAM	W_dust	0	0	99.94	4.15	21.11
	W_weak	19.86	4.26	0	48.40	23.79
	Total	100	100	100	100	100
	W_strong	44.12	4.41	0	3.62	10.79
	W_slight	55.88	95.90	1.39	95.86	21.14
CNN	W_dust	0	0	98.44	0.52	28.74
	W_weak	0	0	0.16	0	39.34
	Total	100	100	100	100	100

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Chipeng Cao, Huiqin Wang, Ke Wang, Zhan Wang, Gang Zhang, Tao Ma. Intelligent Evaluation Method of Grottoes Surface Weathering Based on Multispectral Imaging and Random Forest Algorithm[J]. Acta Optica Sinica, 2020, 40(22): 2230001

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