Cloud mask production method based on improved FCM algorithm for SDGSAT-1 multispectral images

doi:10.7523/j.ucas.2024.077

Abstract

Abstract:

Sustainable development science satellite-1 （SDGSAT-1） is the first earth science satellite of the Chinese Academy of Sciences. It was successfully launched on November 5， 2021， aiming to achieve the global sustainable development goals and provide data for the study of interaction between humans and nature. Due to the poor penetration of visible light through cloud， many optical satellite remote sensing images are inevitably disturbed by cloud. Therefore， cloud mask production is an important processing step in optical remote sensing processing system. However， SDGSAT-1 multispectral images lack the short-wave infrared band required by the traditional cloud mask production algorithm， so the traditional cloud mask production algorithm is difficult to apply. Therefore， this paper proposes a cloud mask production method for SDGSAT-1 multispectral images based on fuzzy C-means （FCM） algorithm. The method selects the four spectral features of brightness， NDWI， NDVI， and HOT as input features. On this basis， the Mahalanobis distance is introduced， the results of K-means algorithm are used as the initial clustering center， and finally FCM algorithm is executed to obtain the final cloud mask. In the paper， the published SDGSAT-1 multispectral images are used to verify the performance of the proposed method and to compare it with other methods. The experimental results show that the average accuracy of the proposed method is 95.33%， which is higher than those of other methods.

Key words: SDGSAT-1, multispectral images, cloud mask production, FCM algorithm, K-means algorithm

CLC Number:

TP751.1

Kaiqiang GE, Jiayin LIU, Feng WANG. Cloud mask production method based on improved FCM algorithm for SDGSAT-1 multispectral images[J]. Journal of University of Chinese Academy of Sciences, 2026, 43(4): 531-540.

Figures/Tables 12

Table 1 Band parameters of the SDGSAT-1 multi-spectral imager

波段	类型	波长/μm
B1	深蓝1	0.374~0.427
B2	深蓝2	0.410~0.467
B3	蓝	0.457~0.529
B4	绿	0.510~0.597
B5	红	0.618~0.696
B6	红边	0.744~0.813
B7	近红外	0.798~0.911

Fig.1 NDWI results

Fig.2 NDVI results

Table 2 Image information

编号	日期	中心经度/（°）	中心纬度/（°）	下垫面类型
图3	2022-04-09	149.01	-31.71	植被
图4	2022-04-10	39.17	10.33	裸地
图5	2022-04-16	-100.02	15.93	水体
图6	2022-04-10	39.72	13.07	植被、裸地

Fig.3 Cloud mask results for vegetation surface

Fig.4 Cloud mask results for bare land underlying surface

Fig.5 Cloud mask results for water surface

Fig.6 Cloud mask results for vegetation and bare land underlying surfaces

Table 3 Overall accuracy of each method for different images

影像	总体精度
影像	MSScvm	HMF	本方法
图3	93.88	94.47	95.27
图4	75.67	94.12	95.48
图5	96.55	96.62	96.73
图6	92.78	94.01	94.46

Fig.7 Overall accuracy histogram of different methods

Fig.8 Comparison of cloud cover between methods and visual interpretation

Table 4 DBI and SC of the proposed method and FCM algorithm

	DBI	SC
传统FCM算法	0.463 4	0.777 5
本文方法	0.412 0	0.791 6

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