高分四号卫星数据云和云阴影检测算法
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摘要
高分四号卫星(GF-4)是我国研制的首颗地球同步高分辨率光学成像卫星,具有高时间分辨率和较高的空间分辨率。针对高分四号卫星数据的特点,提出了一种光谱分析与几何算法相结合的云和云阴影检测算法。使用几何校正和辐射定标后的高分四号影像,基于云与典型地表的光谱特征,采用光谱差异分析技术识别出潜在云像元,根据有云地物和无云地物的光谱变化率差异计算云概率;由云和云阴影的几何关系,并结合传感器参数识别云阴影的投影带,然后根据阴影的光谱特征在投影带中设定基于影像的动态阈值,用于检测云阴影。该算法能较好地识别薄云,而且可以显著提高云阴影的检测精度。采用目视解译法对检测精度进行验证后发现,不同区域类型的云像元识别位置准确,形状完整;将所提云阴影检测方法与云和云阴影匹配算法进行对比后发现,前者识别的云阴影更为精确。
Gaofen-4(GF-4) satellite is the first geosynchronous high-resolution optical imaging satellite developed by China, and it has high temporal resolution and high spatial resolution. Aiming at the characteristics of GF-4 satellite data, we propose a cloud and cloud shadow detection algorithm combining spectral analysis and geometrical algorithms. Geometrically corrected and radiometrically calibrated GF-4 images are used to identify potential cloud pixels using spectral difference analysis techniques based on the spectral characteristics of clouds and typical land surfaces. The cloud probability is calculated according to the difference of spectral variability rate of clouds and cloudless features. The geometrical relationship between clouds and cloud shadows is combined with the sensor parameters to identify the projective regions of cloud shadows. Then the image-based dynamic thresholds are set in the projection regions based on the spectral characteristics of the shadows to detect cloud shadows. This algorithm can better identify thin clouds, and significantly improve the cloud shadow detection accuracy. The visual interpretation method is used to verify the detection accuracy. It finds that cloud pixels recognition in different regions are more accurate and the shapes are relatively complete. Compared with the method of cloud and cloud shadow matching, the dynamic-spectral-threshold algorithm proposed in this paper is more accurate in detecting cloud shadows.
Gaofen-4(GF-4) satellite is the first geosynchronous high-resolution optical imaging satellite developed by China, and it has high temporal resolution and high spatial resolution. Aiming at the characteristics of GF-4 satellite data, we propose a cloud and cloud shadow detection algorithm combining spectral analysis and geometrical algorithms. Geometrically corrected and radiometrically calibrated GF-4 images are used to identify potential cloud pixels using spectral difference analysis techniques based on the spectral characteristics of clouds and typical land surfaces. The cloud probability is calculated according to the difference of spectral variability rate of clouds and cloudless features. The geometrical relationship between clouds and cloud shadows is combined with the sensor parameters to identify the projective regions of cloud shadows. Then the image-based dynamic thresholds are set in the projection regions based on the spectral characteristics of the shadows to detect cloud shadows. This algorithm can better identify thin clouds, and significantly improve the cloud shadow detection accuracy. The visual interpretation method is used to verify the detection accuracy. It finds that cloud pixels recognition in different regions are more accurate and the shapes are relatively complete. Compared with the method of cloud and cloud shadow matching, the dynamic-spectral-threshold algorithm proposed in this paper is more accurate in detecting cloud shadows.
引文
[1] Wang M, Cheng Y F, Chang X L, et al. On-orbit geometric calibration and geometric quality assessment for the high-resolution geostationary optical satellite GaoFen4[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2017, 125:63-77.
[2] Wang M, Cheng Y F, Chang X L, et al. High accuracy on-orbit geometric calibration of geostationary satellite GF4[J]. Acta Geodaetica et Cartographica Sinica,2017, 46(1):53-61.王密,程宇峰,常学立,等.高分四号静止轨道卫星高精度在轨几何定标[J].测绘学报,2017, 46(1):53-61.
[3] Wang Z T, Zhang Y H, Yuan S Y, et al. The aerosol monitoring over Beijing-Tianjin-Hebei region from GF-4 data[J]. Environment and Sustainable Development, 2016, 41(5):113-116.王中挺,张玉环,袁淑云,等.利用高分四号数据监测“京津冀”地区陆地气溶胶[J].环境与可持续发展,2016, 41(5):113-116.
[4] Wang D Z, He H Y. Observation capability and application prospect of GF-4 satellite[J]. Spacecraft Recovery Remote Sensing, 2017, 38(1):98-106.王殿中,何红艳.“高分四号”卫星观测能力与应用前景分析[J].航天返回与遥感,2017, 38(1):98-106.
[5] Xu W, Long X X, Li Q P, et al. Image radiometric and geometric accuracy evaluation of GF-4 satellite[J].Spacecraft Recovery&Remote Sensing, 2016, 37(4):16-25.徐文,龙小祥,李庆鹏,等.“高分四号”卫星影像辐射与几何精度评价[J].航天返回与遥感,2016, 37(4):16-25.
[6] Li G, Kong X H, Liu F J, et al. GF-4 satellite remote sensing technology innovation[J]. Spacecraft Recovery&Remote Sensing, 2016, 37(4):7-15.李果,孔祥皓,刘凤晶,等.“高分四号”卫星遥感技术创新[J].航天返回与遥感,2016, 37(4):7-15.
[7] Sun L, Wei J, Wang J, et al. A universal dynamic threshold cloud detection algorithm(UDTCDA)supported by a prior surface reflectance database[J].Journal of Geophysical Research:Atmospheres,2016, 121(12):7172-7196.
[8] Shen J X,Ji X. Cloud and cloud shadow multifeature collaborative detection from remote sensing image[J]. Journal of Geo-Information Science, 2016,18(5):599-605.沈金祥,季漩.遥感影像云及云影多特征协同检测方法[J].地球信息科学学报,2016, 18(5):599-605.
[9] Sun L, Liu X Y, Yang Y K, et al. A cloud shadow detection method combined with cloud height iteration and spectral analysis for Landsat 8 OLI data[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2018, 138:193-207.
[10] Zhu Z, Woodcock C E. Automated cloud, cloud shadow, and snow detection in multitemporal Landsat data:an algorithm designed specifically for monitoring land cover change[J]. Remote Sensing of Environment,2014, 152:217-234.
[11] Irish R R. Landsat 7 automatic cloud cover assessment[C]. Proceedings of SPIE, 2000:348-356.
[12] Irish R R, Barker J L, Goward S N, et al.Characterization of the Landsat-7 ETM+automated cloud-cover assessment(ACCA)algorithm[J].Photogrammetric Engineering&Remote Sensing,2006, 72(10):1179-1188.
[13] Steve A, Kathleen S, Paul M, et al. Discriminating clear-sky from clouds with MODIS algorithm[J].Theoretical Basis Document(MOD35), 2010, 103(D24):32.
[14] 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.
[15] Chen Y, Fan R S, Wang J X, et al. Cloud detection of ZY-3 satellite remote sensing images based on deep learning[J]. Acta Optica Sinica, 2018,38(1):0128005.陈洋,范荣双,王竞雪,等.基于深度学习的资源三号卫星遥感影像云检测方法[J].光学学报,2018,38(1):0128005.
[16] Zhou W, Guan J, Jiang T, et al. Automatic detection and repairing of cloud and shadow regions in multi-spectral remote sensing images[J]. Journal of Remote Sensing, 2012, 16(1):132-142.周伟,关键,姜涛,等.多光谱遥感影像中云影区域的检测与修复[J].遥感学报,2012, 16(1):132-142.
[17] Oreopoulos L, Wilson M J,Varnai T.Implementation on Landsat data of a simple cloudmask algorithm developed for MODIS land bands[J].IEEE Geoscience and Remote Sensing Letters, 2011,8(4):597-601.
[18] Ri P, Ma Z B, Qi Q W, et al. Cloud and shadow removal from Landsat TM data[J]. Journal of Remote Sensing, 2010, 14(3):534-545.李炳燮,马张宝,齐清文,等.Landsat TM遥感影像中厚云和阴影去除[J].遥感学报,2010, 14(3):534-545.
[19] Qin Y, Deng R R, He Y Q, et al. Algorithm for removing thick clouds in TM image based on spectral and geometric information[J]. Remote Sensing for Land&Resources, 2012, 24(4):55-61.秦雁,邓孺孺,何颖清,等.基于光谱及几何信息的TM图像厚云去除算法[J].国土资源遥感,2012,24(4):55-61.
[20] Knudby A, Latifovic R, Pouliot D. A cloud detection algorithm for AATSR data, optimized for daytime observations in Canada[J]. Remote Sensing of Environment, 2011, 115(12):3153-3164.
[21] Zhu Z, Woodcock C E. Object-based cloud and cloud shadow detection in Landsat imagery[J]. Remote Sensing of Environment, 2012, 118(15):83-94.
[22] 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.
[23] Hagolle O, Huc M, Pascual D V, et al. A multitemporal method for cloud detection, applied to Formosat-2, VENμS, Landsat and Sentinel-2images[J]. Remote Sensing of Environment, 2010,114(8):1747-1755.
[24] Hu C M, Bai Y, Tang P. Automatic cloud detection for GF-4 series images[J]. Journal of Remote Sensing,2018, 22(1):132-142.胡昌苗,白洋,唐娉.GF-4序列图像的云自动检测[J].遥感学报,2018, 22(1):132-142.
[25] Wei J, Sun L, Jia C, et al. Dynamic threshold cloud detection algorithms for MODIS and Landsat 8 data[C].Beijing:2016 IEEE International Geoscience and Remote Sensing Symposium(IGARSS), 2016:566-569.
[26] Baldridge A M, Hook S J, Grove C I, et al. The ASTER spectral library version 2.0[J]. Remote Sensing of Environment, 2009, 113(4):711-715.
[27] Sun L, Mi X T, Wei J, et al. A cloud detection algorithm-generating method for remote sensing data at visible to short-wave infrared wavelengths[J].ISPRS Journal of Photogrammetry and Remote Sensing, 2017, 124:70-88.
[28] Fan W, Wang Y, Rao R Z. Measurement of the atmospheric transmittance from visible to near infrared bands[J]. Acta Photonica Sinica, 2006,35(3):402-407.范伟,王毅,饶瑞中.可见到近红外波段整层大气光谱透过率的测量研究[J].光子学报,2006, 35(3):402-407.
[29] Jiang X Q, Ye Q, Lin Y, et al. Inverting study on soil water content based on harmonic analysis and hyperspectral remote sensing[J]. Acta Optica Sinica,2017, 37(10):1028001.姜雪芹,叶勤,林怡,等.基于谐波分析和高光谱遥感的土壤含水量反演研究[J].光学学报,2017,37(10):1028001.
[30] Liu H J, Zhang B, Zhang Y Z, et al. Soil taxonomy on the basis of reflectance spectral characteristics[J].Spectroscopy and Spectral Analysis, 2008, 28(3):624-628.刘焕军,张柏,张渊智,等.基于反射光谱特性的土壤分类研究[J].光谱学与光谱分析,2008, 28(3):624-628.
[31] Wong M S, Nichol J, Lee K H, et al. Retrieval of aerosol optical thickness using MODIS 500×500 m~2,a study in Hong Kong and Pearl River delta region[C].2008 International Workshop on Earth Observation and Remote Sensing Applications, Beijing, 2008:1-6.
[32] Gomez-Chova L, Camps-Valls G, Calpe-Maravilla J,et al. Cloud-screening algorithm for ENVISAT/MERIS multispectral images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2007, 45(12):4105-4118.
[33] Zhang Y, Guindon B, Cihlar J. An image transform to characterize and compensate for spatial variations in thin cloud contamination of Landsat images[J].Remote Sensing of Environment, 2002, 82(2/3):173-187.
[34] Zhu Z, Wang S X, Woodcock C E. Improvement and expansion of the Fmask algorithm:cloud, cloud shadow, and snow detection for Landsats 4-7, 8, and Sentinel 2 images[J]. Remote Sensing of Environment,2015, 159:269-277.
[35] Wang X P, Song X Q, Chen Y B, et al. Observation and validation of cloud layer structures from the mobile Doppler lidar and radiosonde during spring in Beijing[J]. Acta Optica Sinica, 2015,35(s2):s201001.王箫鹏,宋小全,陈玉宝,等.激光雷达观测北京春季云垂直结构及探空对比研究[J].光学学报,2015,35(s2):s201001.
[36] Liu Z G, Zhou Y X,Shen F. Statistic analysis of suspended sediment concentration in offshore waters based on field measurement of reflectance hyperspectral[J].Journal of Hydraulic Engineering, 2007,38(7):799-805.刘志国,周云轩,沈芳.河口水体泥沙浓度的水面光谱统计模式分析[J].水利学报,2007, 38(7):799-805.
[2] Wang M, Cheng Y F, Chang X L, et al. High accuracy on-orbit geometric calibration of geostationary satellite GF4[J]. Acta Geodaetica et Cartographica Sinica,2017, 46(1):53-61.王密,程宇峰,常学立,等.高分四号静止轨道卫星高精度在轨几何定标[J].测绘学报,2017, 46(1):53-61.
[3] Wang Z T, Zhang Y H, Yuan S Y, et al. The aerosol monitoring over Beijing-Tianjin-Hebei region from GF-4 data[J]. Environment and Sustainable Development, 2016, 41(5):113-116.王中挺,张玉环,袁淑云,等.利用高分四号数据监测“京津冀”地区陆地气溶胶[J].环境与可持续发展,2016, 41(5):113-116.
[4] Wang D Z, He H Y. Observation capability and application prospect of GF-4 satellite[J]. Spacecraft Recovery Remote Sensing, 2017, 38(1):98-106.王殿中,何红艳.“高分四号”卫星观测能力与应用前景分析[J].航天返回与遥感,2017, 38(1):98-106.
[5] Xu W, Long X X, Li Q P, et al. Image radiometric and geometric accuracy evaluation of GF-4 satellite[J].Spacecraft Recovery&Remote Sensing, 2016, 37(4):16-25.徐文,龙小祥,李庆鹏,等.“高分四号”卫星影像辐射与几何精度评价[J].航天返回与遥感,2016, 37(4):16-25.
[6] Li G, Kong X H, Liu F J, et al. GF-4 satellite remote sensing technology innovation[J]. Spacecraft Recovery&Remote Sensing, 2016, 37(4):7-15.李果,孔祥皓,刘凤晶,等.“高分四号”卫星遥感技术创新[J].航天返回与遥感,2016, 37(4):7-15.
[7] Sun L, Wei J, Wang J, et al. A universal dynamic threshold cloud detection algorithm(UDTCDA)supported by a prior surface reflectance database[J].Journal of Geophysical Research:Atmospheres,2016, 121(12):7172-7196.
[8] Shen J X,Ji X. Cloud and cloud shadow multifeature collaborative detection from remote sensing image[J]. Journal of Geo-Information Science, 2016,18(5):599-605.沈金祥,季漩.遥感影像云及云影多特征协同检测方法[J].地球信息科学学报,2016, 18(5):599-605.
[9] Sun L, Liu X Y, Yang Y K, et al. A cloud shadow detection method combined with cloud height iteration and spectral analysis for Landsat 8 OLI data[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2018, 138:193-207.
[10] Zhu Z, Woodcock C E. Automated cloud, cloud shadow, and snow detection in multitemporal Landsat data:an algorithm designed specifically for monitoring land cover change[J]. Remote Sensing of Environment,2014, 152:217-234.
[11] Irish R R. Landsat 7 automatic cloud cover assessment[C]. Proceedings of SPIE, 2000:348-356.
[12] Irish R R, Barker J L, Goward S N, et al.Characterization of the Landsat-7 ETM+automated cloud-cover assessment(ACCA)algorithm[J].Photogrammetric Engineering&Remote Sensing,2006, 72(10):1179-1188.
[13] Steve A, Kathleen S, Paul M, et al. Discriminating clear-sky from clouds with MODIS algorithm[J].Theoretical Basis Document(MOD35), 2010, 103(D24):32.
[14] 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.
[15] Chen Y, Fan R S, Wang J X, et al. Cloud detection of ZY-3 satellite remote sensing images based on deep learning[J]. Acta Optica Sinica, 2018,38(1):0128005.陈洋,范荣双,王竞雪,等.基于深度学习的资源三号卫星遥感影像云检测方法[J].光学学报,2018,38(1):0128005.
[16] Zhou W, Guan J, Jiang T, et al. Automatic detection and repairing of cloud and shadow regions in multi-spectral remote sensing images[J]. Journal of Remote Sensing, 2012, 16(1):132-142.周伟,关键,姜涛,等.多光谱遥感影像中云影区域的检测与修复[J].遥感学报,2012, 16(1):132-142.
[17] Oreopoulos L, Wilson M J,Varnai T.Implementation on Landsat data of a simple cloudmask algorithm developed for MODIS land bands[J].IEEE Geoscience and Remote Sensing Letters, 2011,8(4):597-601.
[18] Ri P, Ma Z B, Qi Q W, et al. Cloud and shadow removal from Landsat TM data[J]. Journal of Remote Sensing, 2010, 14(3):534-545.李炳燮,马张宝,齐清文,等.Landsat TM遥感影像中厚云和阴影去除[J].遥感学报,2010, 14(3):534-545.
[19] Qin Y, Deng R R, He Y Q, et al. Algorithm for removing thick clouds in TM image based on spectral and geometric information[J]. Remote Sensing for Land&Resources, 2012, 24(4):55-61.秦雁,邓孺孺,何颖清,等.基于光谱及几何信息的TM图像厚云去除算法[J].国土资源遥感,2012,24(4):55-61.
[20] Knudby A, Latifovic R, Pouliot D. A cloud detection algorithm for AATSR data, optimized for daytime observations in Canada[J]. Remote Sensing of Environment, 2011, 115(12):3153-3164.
[21] Zhu Z, Woodcock C E. Object-based cloud and cloud shadow detection in Landsat imagery[J]. Remote Sensing of Environment, 2012, 118(15):83-94.
[22] 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.
[23] Hagolle O, Huc M, Pascual D V, et al. A multitemporal method for cloud detection, applied to Formosat-2, VENμS, Landsat and Sentinel-2images[J]. Remote Sensing of Environment, 2010,114(8):1747-1755.
[24] Hu C M, Bai Y, Tang P. Automatic cloud detection for GF-4 series images[J]. Journal of Remote Sensing,2018, 22(1):132-142.胡昌苗,白洋,唐娉.GF-4序列图像的云自动检测[J].遥感学报,2018, 22(1):132-142.
[25] Wei J, Sun L, Jia C, et al. Dynamic threshold cloud detection algorithms for MODIS and Landsat 8 data[C].Beijing:2016 IEEE International Geoscience and Remote Sensing Symposium(IGARSS), 2016:566-569.
[26] Baldridge A M, Hook S J, Grove C I, et al. The ASTER spectral library version 2.0[J]. Remote Sensing of Environment, 2009, 113(4):711-715.
[27] Sun L, Mi X T, Wei J, et al. A cloud detection algorithm-generating method for remote sensing data at visible to short-wave infrared wavelengths[J].ISPRS Journal of Photogrammetry and Remote Sensing, 2017, 124:70-88.
[28] Fan W, Wang Y, Rao R Z. Measurement of the atmospheric transmittance from visible to near infrared bands[J]. Acta Photonica Sinica, 2006,35(3):402-407.范伟,王毅,饶瑞中.可见到近红外波段整层大气光谱透过率的测量研究[J].光子学报,2006, 35(3):402-407.
[29] Jiang X Q, Ye Q, Lin Y, et al. Inverting study on soil water content based on harmonic analysis and hyperspectral remote sensing[J]. Acta Optica Sinica,2017, 37(10):1028001.姜雪芹,叶勤,林怡,等.基于谐波分析和高光谱遥感的土壤含水量反演研究[J].光学学报,2017,37(10):1028001.
[30] Liu H J, Zhang B, Zhang Y Z, et al. Soil taxonomy on the basis of reflectance spectral characteristics[J].Spectroscopy and Spectral Analysis, 2008, 28(3):624-628.刘焕军,张柏,张渊智,等.基于反射光谱特性的土壤分类研究[J].光谱学与光谱分析,2008, 28(3):624-628.
[31] Wong M S, Nichol J, Lee K H, et al. Retrieval of aerosol optical thickness using MODIS 500×500 m~2,a study in Hong Kong and Pearl River delta region[C].2008 International Workshop on Earth Observation and Remote Sensing Applications, Beijing, 2008:1-6.
[32] Gomez-Chova L, Camps-Valls G, Calpe-Maravilla J,et al. Cloud-screening algorithm for ENVISAT/MERIS multispectral images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2007, 45(12):4105-4118.
[33] Zhang Y, Guindon B, Cihlar J. An image transform to characterize and compensate for spatial variations in thin cloud contamination of Landsat images[J].Remote Sensing of Environment, 2002, 82(2/3):173-187.
[34] Zhu Z, Wang S X, Woodcock C E. Improvement and expansion of the Fmask algorithm:cloud, cloud shadow, and snow detection for Landsats 4-7, 8, and Sentinel 2 images[J]. Remote Sensing of Environment,2015, 159:269-277.
[35] Wang X P, Song X Q, Chen Y B, et al. Observation and validation of cloud layer structures from the mobile Doppler lidar and radiosonde during spring in Beijing[J]. Acta Optica Sinica, 2015,35(s2):s201001.王箫鹏,宋小全,陈玉宝,等.激光雷达观测北京春季云垂直结构及探空对比研究[J].光学学报,2015,35(s2):s201001.
[36] Liu Z G, Zhou Y X,Shen F. Statistic analysis of suspended sediment concentration in offshore waters based on field measurement of reflectance hyperspectral[J].Journal of Hydraulic Engineering, 2007,38(7):799-805.刘志国,周云轩,沈芳.河口水体泥沙浓度的水面光谱统计模式分析[J].水利学报,2007, 38(7):799-805.