CDAG-Improved Algorithm and Its Application to GF-6 WFV Data Cloud Detection

Zhen Dong; Lin Sun; Xirong Liu; Yongji Wang; Tianchen Liang

doi:10.3788/AOS202040.1628001

Journals >Acta Optica Sinica >Volume 40 >Issue 16 >Page 1628001 > Article

Acta Optica Sinica
Vol. 40, Issue 16, 1628001 (2020)

CDAG-Improved Algorithm and Its Application to GF-6 WFV Data Cloud Detection

Zhen Dong, Lin Sun^*, Xirong Liu, Yongji Wang, and Tianchen Liang

Author Affiliations

College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao, Shandong 266590, China

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DOI: 10.3788/AOS202040.1628001 Cite this Article Set citation alerts

Zhen Dong, Lin Sun, Xirong Liu, Yongji Wang, Tianchen Liang. CDAG-Improved Algorithm and Its Application to GF-6 WFV Data Cloud Detection[J]. Acta Optica Sinica, 2020, 40(16): 1628001 Copy Citation Text

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Comparison of AVIRIS(solid line) and GF-6 WFV(dashed line) data spectral response curves at different bands. (a) 1st band; (b) 2nd band; (c) 4th band; (d) 6th band

Fig. 1. Comparison of AVIRIS(solid line) and GF-6 WFV(dashed line) data spectral response curves at different bands. (a) 1st band; (b) 2nd band; (c) 4th band; (d) 6th band

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Fig. 2. Comparison of raw image and simulated image of the Wisconsin area, USA. (a) Raw GF-6 WFV image; (b) simulated GF-6 WFV image

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Fig. 3. Flow chart of GF-6 WFV data cloud detection algorithm

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Fig. 6. Partial image vectorization results. (a) Vectorization result of vegetation image; (b) vectorization result of urban area image

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Table 1. Typical examples of bright surface pixels in the pixel dataset

Single band	Multi band	Band ratio
B₁>0.27	B₁>0.18 & B₄>0.34	0.80<B₁/B₇<1.08
B₇>0.26	B₁>0.22 & B₇>0.26	0.99<B₂/B₈<1.20
	B₂>0.14 & B₄>0.36	0.47<B₃/B₇<1.14
	B₂>0.18 & B₇>0.26	0.91<B₃/B₈<1.07
	B₃>0.14 & B₆>0.32
	B₃>0.14 & B₇>0.26
	B₄>0.36 & B₅>0.20
	B₄>0.36 & B₈>0.18
	B₅>0.20 & B₆>0.32

Table 2. Band combination and threshold selected under the original CDAG method

Band	Average	Multi-band dispersion
B₁,B₂,B₅	A₁=(B₁+B₂+B₅)/3	[(B₁-A₁)/A₁+(B₂-A₁)/A₁+(B₅-A₁)/A₁]<0.015
B₁,B₂,B₅	A₂=(B₁+B₃+B₇)/3	[(B₁-A₂)/A₂+(B₃-A₂)/A₂+(B₇-A₂)/A₂]<0.023
B₁,B₅,B₇	A₃=(B₁+B₅+B₇)/3	[(B₁-A₃)/A₃+(B₅-A₃)/A₃+(B₇-A₃)/A₃]<0.057
B₂,B₃,B₇	A₄=(B₂+B₃+B₇)/3	[(B₂-A₄)/A₄+(B₃-A₅)/A₄+(B₇-A₄)/A₄]<0.018
B₂,B₅,B₇	A₅=(B₂+B₅+B₇)/3	[(B₂-A₅)/A₅+(B₅-A₅)/A₅+(B₇-A₅)/A₅]<0.055
B₃,B₅,B₇	A₆=(B₃+B₅+B₇)/3	[(B₃-A₆)/A₆+(B₅-A₆)/A₆+(B₇-A₆)/A₆]<0.043

Table 3. Band and threshold selected by three-band dispersion combination

Multi band	Multi band+Band difference
B₁>0.30 & B₂>0.32 & B₇>0.28	B₁>0.31 & B₃>0.35 & B₃-B₇<0.015
B₁>0.31 & B₃>0.36 & B₇>0.27	B₁>0.31 & B₄>0.39 & B₄-B₇<0.075
B₂>0.33 & B₃>0.37 & B₇>0.28	B₂>0.34 & B₆>0.35 & B₆-B₇<0.035
B₂>0.34 & B₅>0.35 & B₇>0.29

Table 4. Band and threshold selected by the bright surface detection algorithm

Underlying surface	Image	C_R /%	E_R /%	M_R /%	S_R /%
	1	76.58	0.48	23.42	99.52
Bare soil	2	88.45	9.73	11.55	91.27
	3	84.14	2.49	15.86	97.51
	1	85.46	2.79	14.54	97.21
Building	2	88.63	7.38	11.37	92.62
	3	83.79	3.42	16.21	96.58
	1	90.36	0.28	9.64	99.72
Vegetation	2	90.87	0.66	9.13	99.34
	3	88.04	0.15	11.96	99.85
	1	82.55	0.53	17.45	99.47
Water	2	77.66	0.20	22.34	99.80
	3	85.43	0.61	14.57	99.39
Total		85.16	2.39	14.84	97.69

Table 5. Accuracy evaluation of cloud pixel detection results

Zhen Dong, Lin Sun, Xirong Liu, Yongji Wang, Tianchen Liang. CDAG-Improved Algorithm and Its Application to GF-6 WFV Data Cloud Detection[J]. Acta Optica Sinica, 2020, 40(16): 1628001

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