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Cloud Detection Method in GaoFen-2 Multi-spectral Imagery

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Proceedings of the 6th China High Resolution Earth Observation Conference (CHREOC 2019) (CHREOC 2019)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 657))

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Abstract

Cloud cover is one of the major factors which affect the application of GaoFen-2 imagery. Cloud detection in GaoFen-2 imagery is fairly difficult due to the lack of enough infrared bands. This paper presents a cloud detection method for GaoFen-2 multi-spectral imagery based on the radiation transmission model. The scattering coefficient of remote sensing image is estimated by using radiation transmission, and then the cloud mask was obtained by combining the geometric and texture features in high-resolution remote sensing images. Experiments on GaoFen-2 multi-spectral images show that the accuracy of cloud detection is above 94.70%. The method proposed in this paper can effectively reduce the influence of highlighted buildings during cloud detection and achieve a high accuracy for GaoFen-2 imagery cloud detection with less bands. In addition, this paper provides an alternative distinction method for the quantitative researches of thick and thin clouds in optical satellite imagery.

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References

  1. Chen B, Huang B, Xu B (2017) Multi-source remotely sensed data fusion for improving land cover classification. ISPRS J Photogramm 124:27–39

    Article  Google Scholar 

  2. Pellikka PKE, Heikinheimo V, Hietanen J, Schäfer E, Siljander M, Heiskanen J (2018) Impact of land cover change on aboveground carbon stocks in Afromontane landscape in Kenya. Appl Geogr 94:178–189

    Article  Google Scholar 

  3. Romijn E, Herold M, Kooistra L, Murdiyarso D, Verchot L (2012) Assessing capacities of non-annex I countries for national forest monitoring in the context of REDD+. Environ Sci Policy 19–20:33–48

    Article  Google Scholar 

  4. Duan H, Cao Z, Shen M, Liu D, Xiao Q (2019) Detection of illicit sand mining and the associated environmental effects in China’s fourth largest freshwater lake using daytime and nighttime satellite images. Sci Total Environ 647:606–618

    Article  Google Scholar 

  5. Turner J, Marshall GJ, Ladkin RS (2001) An operational, real-time cloud detection scheme for use in the Antarctic based on AVHRR data

    Google Scholar 

  6. Zhang X, Tan S, Shi G, Wang H (2019) Improvement of MODIS cloud mask over severe polluted eastern China. Sci Total Environ 654:345–355

    Article  Google Scholar 

  7. Tang H, Yu K, Hagolle O, Jiang K, Geng X, Zhao Y (2013) A cloud detection method based on a time series of MODIS surface reflectance images. Int J Digit Earth 6:157–171

    Article  Google Scholar 

  8. Jouybari Moghaddam Y, Aghamohamadnia M (2013) A novel method for cloud detection in modis imagery

    Google Scholar 

  9. Zhu Z, Woodcock CE (2012) Object-based cloud and cloud shadow detection in landsat imagery. Remote Sens Environ 118:83–94

    Article  Google Scholar 

  10. Huang C, Thomas N, Goward SN, Masek JG, Zhu Z, Townshend JRG et al (2010) Automated masking of cloud and cloud shadow for forest change analysis using landsat images. Int J Remote Sens 31:5449–5464

    Article  Google Scholar 

  11. Richard RI, John LB, Samuel NG, Terry A (2006) Characterization of the Landsat-7 ETM automated cloud-cover assessment (ACCA) algorithm

    Google Scholar 

  12. Fisher A (2014) Cloud and cloud-shadow detection in SPOT5 HRG imagery with automated morphological feature extraction. Remote Sens-Basel 6:776–800

    Article  Google Scholar 

  13. Li Z, Shen H, Li H, Xia G, Gamba P, Zhang L (2017) Multi-feature combined cloud and cloud shadow detection in GaoFen-1 wide field of view imagery. Remote Sens Environ 191:342–358

    Article  Google Scholar 

  14. Zhai H, Zhang H, Zhang L, Li P (2018) Cloud/shadow detection based on spectral indices for multi/hyperspectral optical remote sensing imagery. ISPRS J Photogramm 144:235–253

    Article  Google Scholar 

  15. Zhu X, Helmer EH (2018) An automatic method for screening clouds and cloud shadows in optical satellite image time series in cloudy regions. Remote Sens Environ 214:135–153

    Article  Google Scholar 

  16. Foga S, Scaramuzza PL, Guo S, Zhu Z, Dilley RD, Beckmann T et al (2017) Cloud detection algorithm comparison and validation for operational landsat data products. Remote Sens Environ 194:379–390

    Article  Google Scholar 

  17. Le Hégarat-Mascle S, André C (2009) Use of Markov random fields for automatic cloud/shadow detection on high resolution optical images. ISPRS J Photogramm 64:351–366

    Article  Google Scholar 

  18. Ishida H, Oishi Y, Morita K, Moriwaki K, Nakajima TY (2018) Development of a support vector machine based cloud detection method for MODIS with the adjustability to various conditions. Remote Sens Environ 205:390–407

    Article  Google Scholar 

  19. Xie F, Shi M, Shi Z, Yin J, Zhao D (2017) Multilevel cloud detection in remote sensing images based on deep learning. IEEE J-Stars 10:3631–3640

    Google Scholar 

  20. Champion N (2012) Automatic cloud detection from multi-temporal satellite images: towards the use of Pléiades time series

    Google Scholar 

  21. Lin C, Lin B, Lee K, Chen Y (2015) Radiometric normalization and cloud detection of optical satellite images using invariant pixels. ISPRS J Photogramm 106:107–117

    Article  Google Scholar 

  22. Zhu Z, Wang S, Woodcock CE (2015) Improvement and expansion of the Fmask algorithm: cloud, cloud shadow, and snow detection for Landsats 4–7, 8, and Sentinel 2 images. Remote Sens Environ 159:269–277

    Article  Google Scholar 

  23. Fattal R (2008) Single image dehazing. ACM Trans Graph (TOG) 27:1–9

    Article  Google Scholar 

  24. Fabio C, Eric K (1997) Depth from scattering

    Google Scholar 

  25. He K, Sun J, Tang X (2011) Single image haze removal using dark channel prior. IEEE Trans Pattern Anal Mach Intell 33:2341–2353

    Article  Google Scholar 

  26. Jain AK (2010) Data clustering: 50 years beyond K-means. Pattern Recogn Lett 31:651–666

    Article  Google Scholar 

  27. Zahra S, Ghazanfar MA, Khalid A, Azam MA, Naeem U, Prugel-Bennett A (2015) Novel centroid selection approaches for K means-clustering based recommender systems. Inform Sci 320:156–189

    Article  MathSciNet  Google Scholar 

  28. McFEETERS SK (1996) The use of the normalized difference water index (NDWI) in the delineation of open water features. Int J Remote Sens 17:1425–1432

    Article  Google Scholar 

  29. Zhang X, Xiao P, Feng X, Yuan M (2017) Separate segmentation of multi-temporal high-resolution remote sensing images for object-based change detection in urban area. Remote Sens Environ 201:243–255

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Remote Sensing Information Engineering, Wuhan, Hubei, China

    Zhaocong Wu, Lin He, Yi Zhang & Jun Li

Authors
  1. Zhaocong Wu
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  2. Lin He
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  3. Yi Zhang
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  4. Jun Li
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Corresponding author

Correspondence to Lin He .

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Editors and Affiliations

  1. China Aerospace Science and Technology Corporation, Beijing, China

    Liheng Wang

  2. Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China

    Yirong Wu

  3. Wuhan University, Wuhan, China

    Jianya Gong

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Wu, Z., He, L., Zhang, Y., Li, J. (2020). Cloud Detection Method in GaoFen-2 Multi-spectral Imagery. In: Wang, L., Wu, Y., Gong, J. (eds) Proceedings of the 6th China High Resolution Earth Observation Conference (CHREOC 2019). CHREOC 2019. Lecture Notes in Electrical Engineering, vol 657. Springer, Singapore. https://doi.org/10.1007/978-981-15-3947-3_15

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  • DOI: https://doi.org/10.1007/978-981-15-3947-3_15

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-3946-6

  • Online ISBN: 978-981-15-3947-3

  • eBook Packages: EngineeringEngineering (R0)

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