动态阈值云检测算法改进及在高分辨率卫星上的应用
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摘要
基于先验地表反射率数据库支持的动态阈值云检测算法(UDTCDA)可以显著提高卫星数据的云检测精度。为进一步提高其在波段相对较少的高空间分辨率卫星数据云检测应用中的精度,改进了UDTCDA中先验地表反射率数据与待检测卫星数据的空间匹配方法。与原方法使用重采样达到空间分辨率一致不同,该方法根据待检测影像高空间分辨率的特点,采用逐像元空间地理坐标配准的方法与真实地表反射率数据进行配准,然后进行云像元检测。该方法保留了高分辨率影像空间分辨率的优势,可以有效降低空间重采样造成的像元信息丢失。分别使用资源3号、高分1号、高分2号和高分4号高分辨率卫星数据开展云检测实验。通过遥感目视解译的方法对结果进行精度验证,并与UDTCDA云识别结果进行对比。结果表明,改进后的算法能以较高的精度识别不同高分辨率卫星影像中的云,总体精度可达到93.92%,对于碎云和薄云具有整体较高的识别精度,漏分误差和错分误差分别低于10.40%和9.57%。
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 prior surface reflectance and the satellite observed reflectance.Different with 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 of93.92%.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.
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 prior surface reflectance and the satellite observed reflectance.Different with 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 of93.92%.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.
引文
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[2]Hégarat-Mascle S L,AndréC.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.蔡熠,刘延利,戴聪明,等.卷云大气条件下目标与背景对比度模拟分析[J].光学学报,2017,37(8):0801001.
[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.毛飞跃,龚威,李俊,等.基于改进微分零交叉法的米氏散射激光雷达云检测与参数反演[J].光学学报,2010,30(11):3097-3102.
[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,etal.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.张海龙,孙德勇,李俊生,等.基于GF1-WFV和HJ-CCD数据的我国近海绿潮遥感监测算法研究[J].光学学报,2016,36(6):0601004.
[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.MOD09users guide[EB/OL].[2018-02-10].http://modis-sr.Itdri.org.
[19]Vermote E F,Kotchenova S Y,Ray J P.MODISsurface reflectance users 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.孙林,于会泳,傅俏燕,等.地表反射率产品支持的GF-1PMS气溶胶光学厚度反演及大气校正[J].遥感学报,2016,20(2):216-228.
[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.刘大伟,韩玲,韩晓勇.基于深度学习的高分辨率遥感影像分类研究[J].光学学报,2016,36(4):0428001.
[24]Kokaly R F,Clark R N,Swayze G A,et al.USGSspectral 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.韦晶,明艳芳,韩留生,等.基于多类型光谱特征参数匹配的矿物信息遥感识别方法[J].光谱学与光谱分析,2015,35(10):2862-2866.
[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.