GF-6 WFV Data Cloud Detection Based on Improved LCCD Algorithm

Yongji Wang; Yanfang Ming; Tianchen Liang; Xueying Zhou; Chen Jia; Quan Wang

doi:10.3788/AOS202040.2128001

Journals >Acta Optica Sinica >Volume 40 >Issue 21 >Page 2128001 > Article

Acta Optica Sinica
Vol. 40, Issue 21, 2128001 (2020)

GF-6 WFV Data Cloud Detection Based on Improved LCCD Algorithm

Yongji Wang¹, Yanfang Ming^1、*, Tianchen Liang¹, Xueying Zhou², Chen Jia¹, and Quan Wang¹

Author Affiliations

¹College of Geomatics, Shandong University of Science and Technology, Qingdao, Shandong 266590, China

²School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, Hubei 430072, China

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DOI: 10.3788/AOS202040.2128001 Cite this Article Set citation alerts

Yongji Wang, Yanfang Ming, Tianchen Liang, Xueying Zhou, Chen Jia, Quan Wang. GF-6 WFV Data Cloud Detection Based on Improved LCCD Algorithm[J]. Acta Optica Sinica, 2020, 40(21): 2128001 Copy Citation Text

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Fig. 1. Surface reflectance characteristics of non-spatiotemporal series. (a) Water; (b) wetland; (c) bareland; (d) impervious surface

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Fig. 2. Surface reflectance characteristics of spatiotemporal series. (a) Forest; (b) grassland; (c) shrubland

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Fig. 3. Flow chart of cloud detection

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Fig. 4. Cloud detection results above water. (a) Thick cloud; (b) broken cloud; (c) thin cloud

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Fig. 5. Cloud detection results above vegetation. (a) Thick cloud; (b) thin cloud; (c) broken cloud

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Fig. 6. Cloud detection results above bright surfaces. (a) Thin cloud; (b) thick cloud; (c) broken cloud

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Fig. 7. Landsat8 cloud detection results above bright surfaces. (a) Broken cloud; (b) thick cloud; (c) thin cloud

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Fig. 8. Accuracy index for each sample. (a) β_CRT; (b) β_CRC; (c) β_CRS

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Fig. 9. Error rate index of each sample. (a) β_CER; (b) β_OER

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Band number	Spectral range /μm	Spatial resolution /m
B01	0.45-0.52	16
B02	0.52-0.59	16
B03	0.63-0.69	16
B04	0.77-0.89	16
B05	0.69-0.73	16
B06	0.73-0.77	16
B07	0.40-0.45	16
B08	0.59-0.63	16

Table 1. Introduction of GF-6 WFV data

Parameter	Forest	Bareland	Tropical	Frigid zone	Temperate zone
Parameter	Forest	Bareland	Tropical	Frigid zone	Spring	Summer	Autumn	Winter
N_s/N_f			0.1/0.9	0.3/0.7	0.6/0.4	0.1/0.9	0.4/0.6	0.9/0.1
T_B	0.12	0.18	0.126	0.138	0.156	0.126	0.144	0.174
T_G	0.18	0.20	0.182	0.186	0.192	0.182	0.188	0.198
T_R	0.13	0.25	0.142	0.166	0.202	0.142	0.178	0.238
Parameter	Grassland	Bareland	Tropical	Frigid zone	Temperate zone
Parameter	Grassland	Bareland	Tropical	Frigid zone	Spring	Summer	Autumn	Winter
N_s/N_g			0.1/0.9	0.7/0.3	0.6/0.4	0.1/0.9	0.4/0.6	0.9/0.1
T_B	0.20	0.18	0.198	0.186	0.188	0.198	0.192	0.182
T_G	0.23	0.20	0.227	0.209	0.212	0.227	0.218	0.203
T_R	0.30	0.25	0.295	0.265	0.270	0.295	0.280	0.255
Parameter	Shrubland	Bareland	Tropical	Frigid zone	Temperate zone
Parameter	Shrubland	Bareland	Tropical	Frigid zone	Spring	Summer	Autumn	Winter
N_s/N_b			0.1/0.9	0.4/0.6	0.6/0.4	0.1/0.9	0.4/0.6	0.9/0.1
T_B	0.16	0.18	0.162	0.168	0.172	0.162	0.168	0.178
T_G	0.18	0.20	0.182	0.188	0.192	0.182	0.188	0.198

Table 2. Thresholds of forests, grasslands and shrublands

Accuracy	Vegetation	Water	Bright surface	Average
β_CRT /%	96.36	96.43	94.01	95.60
β_CRC /%	93.09	95.60	88.70	92.46
β_CRS /%	97.38	96.35	96.67	96.80
β_CER /%	2.59	3.46	2.72	2.92
β_OER /%	6.91	3.77	10.79	7.16

Table 3. Overall accuracy evaluation of cloud detection

Yongji Wang, Yanfang Ming, Tianchen Liang, Xueying Zhou, Chen Jia, Quan Wang. GF-6 WFV Data Cloud Detection Based on Improved LCCD Algorithm[J]. Acta Optica Sinica, 2020, 40(21): 2128001

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