Improvement of Universal Dynamic Threshold Cloud Detection Algorithm and Its Application in High-Resolution Satellite

WANG Quan1 SUN Lin1 WEI Jing1 ZHOU Xueying1 CHEN Tingting1 SHU Meiyan1

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

【Abstract】With the support of a pre-calculated land surface reflectance database, the universal dynamic threshold cloud detection algorithm (UDTCDA) can significantly improve the cloud detection accuracy of satellite data. To further improve its precision in the application of cloud detection for high-spatial-resolution satellite data with relatively few bands, we improve the spatial matching method between the priori surface reflectance and the satellite observed reflectance. Different from the directly resample method in the UDTCDA, the pixel-by-pixel registration method is adopted to realize the matching between the satellite image and surface reflectance image. This approach preserves the spatial resolution advantage of high resolution images, and effectively reduces the loss of pixel information caused by spatial resampling. Four high-resolution satellite data, namely ZY-3, GF-1, GF-2 and GF-4, are used in cloud detection experiments. The cloud detection results of the improved UDTCDA are verified by the visual interpretation cloud results, and compared with the original UDTCDA cloud results. Results show that the improved algorithm can accurately identify different kinds of clouds in different high-resolution satellite images with an average accuracy of 93.92%. Especially for the broken and thin clouds, the accuracy is significantly improved with overall low omission and commission errors less than 10.40% and 9.57%, respectively.

【Keywords】 remote sensing; cloud detection; universal dynamic threshold cloud detection algorithm (UDTCDA) ; high spatial resolution;

【DOI】

【Funds】 National Natural Science Foundation of China (41771408) Natural Science Foundation of Shandong Province (ZR201702210379)

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    References

    [1] Tseng D C, Tseng H T, Chien C L. Automatic cloud removal from multi-temporal SPOT images [J]. Applied Mathematics and Computation, 2008, 205 (2): 584–600.

    [2] Hégarat-Mascle S L, André C. Use of Markov random fields for automatic cloud/shadow detection on high resolution optical images [J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2009, 64 (4): 351–366.

    [3] Goodwin N R, Collett L J, Denham R J, et al. Cloud and cloud shadow screening across Queensland, Australia: an automated method for Landsat TM/ETM+time series [J]. Remote Sensing of Environment, 2013, 134: 50–65.

    [4] Shahtahmassebi A, Yang N, Wang K, et al. Review of shadow detection and de-shadowing methods in remote sensing [J]. Chinese Geographical Science, 2013, 23 (4): 403–420.

    [5] Cai Y, Liu Y L, Dai C M, et al. Simulation analysis of target and background contrast in condition of cirrus atmosphere [J]. Acta Optica Sinica, 2017, 37 (8): 0801001 (in Chinese).

    [6] Mao F Y, Gong W, Li J, et al. Cloud detection and parameter retrieval based on improved differential zero-crossing method for Mie lidar [J]. Acta Optica Sinica, 2010, 30 (11): 3097–3102 (in Chinese).

    [7] Liu W, Yamazaki F. Object-based shadow extraction and correction of high-resolution optical satellite images [J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2012, 5 (4): 1296–1302.

    [8] Jedlovec G J, Haines S L, LaFontaine F J. Spatial and temporal varying thresholds for cloud detection in GOES imagery [J]. IEEE Transactions on Geoscience and Remote Sensing, 2008, 46 (6): 1705–1717.

    [9] Hagolle O, Huc M, Pascual D V, et al. A multitemporal method for cloud detection, applied to FORMOSAT-2, VENμS, LANDSAT and SENTINEL-2 images [J]. Remote Sensing of Environment, 2010, 114 (8): 1747–1755.

    [10] Rossow W B, Mosher F, Kinsella E, et al. ISCCPcloud algorithm intercomparison [J]. Journal of Applied Meteorology, 1985, 24 (9): 877–903.

    [11] Rossow W B, Schiffer R A. ISCCP cloud data products [J]. Bulletin of the American Meteorological Society, 1991, 72 (1): 2-20.

    [12] Rossow W B, Garder L C. Cloud detection using satellite measurements of infrared and visible radiances for ISCCP [J]. Journal of Climate, 1993, 6 (12): 2341–2369.

    [13] Stowe L L, McClain E P, Carey R, et al. Global distribution of cloud cover derived from NOAA/AVHRR operational satellite data [J]. Advances in Space Research, 1991, 11 (3): 51–54.

    [14] Saunders R W, Kriebel K T. An improved method for detecting clear sky and cloudy radiances from AVHRR data [J]. International Journal of Remote Sensing, 1988, 9 (1): 123–150.

    [15] Sun L, Wei J, Wang J, et al. A universal dynamic threshold cloud detection algorithm (UDTCDA) supported by aprior surface reflectance database [J]. Journal of Geophysical Research: Atmospheres, 2016, 121 (12): 7172–7196.

    [16] Zhang H L, Sun D Y, Li J S, et al. Remote sensing algorithm for detecting green tide in china coastal waters based on GF1-WFV and HJ-CCD data [J]. Acta Optica Sinica, 2016, 36 (6): 0601004 (in Chinese).

    [17] Vermote E F, Vermeulen A. Atmospheric correction algorithm: spectral reflectances (MOD09) [M]. [S.l.]: US National Aeronautics and Space Administration.

    [18] Vermote E F, Kotchenova S Y. MOD09 user’s guide [EB/OL]. [2018–02–10]. http: //modis-sr.Itdri.org.

    [19] Vermote E F, Kotchenova S Y, Ray J P. MODIS surface reflectance user’s guide[EB/OL]. [2018-02-10]. http: //www.patarnott.com/satsens/pdf/MOD09_UserGuide_v1_2.pdf.

    [20] Sun L, Yu H Y, Fu Q Y, et al. Aerosol optical depth retrieval and atmospheric correction application for GF-1PMS supported by land surface reflectance data [J]. Journal of Remote Sensing, 2016, 20 (2): 216–228 (in Chinese).

    [21] Levy R C, Mattoo S, Munchak L A, et al. The collection 6 MODIS aerosol products over land and ocean [J]. Atmospheric Measurement Techniques, 2013, 6 (11): 2989–3034.

    [22] Sun L, Wei J, Bilal M, et al. Aerosol optical depth retrieval over bright areas using Landsat 8 OLIimages [J]. Remote Sensing, 2015, 8 (1): 23.

    [23] Liu D W, Han L, Han X Y. High spatial resolution remote sensing image classification based on deep learning [J]. Acta Optica Sinica, 2016, 36 (4): 0428001 (in Chinese).

    [24] Kokaly R F, Clark R N, Swayze G A, et al. USGS spectral library version 7-[EB/OL]. [2018-02-10]. https: //pubs.er.usgs.gov/publication/ds1035.

    [25] Wei J, Ming Y F, Han L S, et al. Method of remote sensing identification for mineral types based on multiple spectral characteristic parameters matching [J]. Spectroscopy and Spectral Analysis, 2015, 35 (10): 2862–2866 (in Chinese).

    [26] Congalton R G. A review of assessing the accuracy of classifications of remotely sensed data [J]. Remote Sensing of Environment, 1991, 37 (1): 35–46.

This Article

ISSN:0253-2239

CN:31-1252/O4

Vol 38, No. 10, Pages 376-385

October 2018

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Article Outline

Abstract

  • 1 Introduction
  • 2 Data source and pre-processing
  • 3 Improved UDTCDA
  • 4 Experimental results and analysis
  • 5 Conclusion
  • References