Enhanced multi-temporal cloud detection algorithm for optical remote sensing images

Xidong CHEN; Xiao ZHANG; Liangyun LIU; Xiaofan WANG

doi:10.11834/jrs.20198017

Technology and Methodology | Views : 0 下载量: 123 CSCD: 9 更多指标

PDF
Export
Share
Collection
Album

Enhanced multi-temporal cloud detection algorithm for optical remote sensing images
Vol. 23, Issue 2, Pages: 280-290(2019)
Published： 2019-3 ，

Accepted： 7 May 2018
DOI： 10.11834/jrs.20198017

扫描看全文

陈曦东, 张肖, 刘良云, 汪晓帆. 2019. 增强型多时相云检测. 遥感学报, 23(2): 280–290

Chen X D, Zhang X, Liu L Y and Wang X F. 2019. Enhanced multi-temporal cloud detection algorithm for optical remote sensing images. Journal of Remote Sensing, 23(2): 280–290
陈曦东, 张肖, 刘良云, 汪晓帆. 2019. 增强型多时相云检测. 遥感学报, 23(2): 280–290 DOI： 10.11834/jrs.20198017.

Chen X D, Zhang X, Liu L Y and Wang X F. 2019. Enhanced multi-temporal cloud detection algorithm for optical remote sensing images. Journal of Remote Sensing, 23(2): 280–290 DOI： 10.11834/jrs.20198017.

摘要

针对云检测在高亮度地表以及雪覆盖区域存在过度检测的问题，设计了一种不依赖热红外波段的增强型多时相云检测EMTCD(Enhanced Multiple Temporal Cloud Detection)算法。首先，利用云的光谱特征建立单时相云检测规则，并基于云、雪的光谱差异构建了增强型云指数ECI(Enhanced Cloud Index)，改进了云、雪的区分能力；其次，以同一区域无云影像为参考，基于ECI指数构建了多时相云检测算法，较好地克服了单时相云检测中高亮度地表、雪和云容易混淆的问题，提高了云检测的精度；最后，选择两个典型区域的Landsat-8 OLI影像，对比分析了不同算法的云检测结果。实验结果表明：ECI指数能够有效区分云、雪，EMTCD方法的平均检测精度达到93.2%，高于Fmask(Function of mask)(81.85%)、MTCD(Multi-Temporal Cloud Detection)(66.14%)和Landsat-8地表反射率产品LaSRC(Landsat-8 Surface Reflectance Code)的云检测结果(86.3%)。因此，本文提出的EMTCD云检测算法能够有效地减少高亮度地表和雪的干扰，实现不依赖热红外波段的高精度云检测。

Abstract

Remote sensing images have been a crucial data source for land cover mapping and other applications. However

optical remote sensing images are frequently contaminated with clouds. Clouds have caused several limitations in remote sensing applications through optical satellite. Although several approaches have been conducted for cloud detection

they still fail to distinguish bright surfaces

snow

and clouds

especially for seasonally snow-covered images. Therefore

we aim to develop a fast and universal cloud detection method

which can accurately detect clouds in complex areas. Considering that many sensors do not have a thermal infrared band

we only use the visible

near infrared

and short-wave infrared bands to detect clouds. The proposed method is expected to be used for a variety of satellite data. In this study

a multi-temporal cloud detection method was proposed for optical images. Given that snow and clouds have a big difference in short-wave infrared bands

we first developed an Enhanced Cloud Index (ECI) based on the spectral properties of the bands to distinguish them. Then

we proposed an Enhanced Multi-Temporal Cloud Detection (EMTCD) algorithm based on the ECI index and multi-temporal images to extract cloudy pixels. Finally

we tested and compared the algorithm with three classical cloud detection algorithms

namely

Function of mask (Fmask)

Cloud Cover Assessment (CCA)

and Multi-Temporal Cloud Detection (MTCD) algorithms

to verify the accuracy of the proposed algorithm. Landsat-8 images were used as the data source in this study. Given that many operational cloud detection methods had failed in complex areas

we selected four Landsat-8 OLI scenes in two test areas with typical seasonal snow cover and complicated land covers as our test data. The images were all obtained from 2015. The test areas were the northeast and southwest of China. Test results indicated that the ECI index can effectively distinguish snow and clouds. The ECI index of snow was higher than that of clouds. The EMTCD method performed well in cloud detection

which had the best cloud detection result with an overall accuracy of 93.2% compared with that of 81.85%

66.14%

and 86.3% for the classic Fmask

MTCD

and CCA cloud detection methods

respectively. The ECI index is effective in distinguishing clouds and snow. The EMTCD algorithm can provide a good performance in cloud detection without using the thermal infrared band

even for seasonally snow-covered regions with complicated high brightness ground surface

which is always challenging for traditional cloud detection algorithms. However

the method is developed based on multiple images. Compared with single temporal methods

the proposed method still has some limitations.

关键词

云检测增强型云指数多时相Landsat-8多光谱

Keywords

cloud detectionEnhanced Cloud Indexmulti-temporalLandsat-8multi-spectral

references

Ackerman S A, Strabala K I, Menzel W P, Frey R A, Moeller C C and Gumley L E. 1998. Discriminating clear sky from clouds with MODIS. Journal of Geophysical Research: Atmospheres, 103(D24): 32141–32157

Braaten J D, Cohen W B and Yang Z Q. 2015. Automated cloud and cloud shadow identification in Landsat MSS imagery for temperate ecosystems. Remote Sensing of Environment, 169: 128–138

Candra D S, Phinn S and Scarth P. 2016. Cloud and cloud shadow masking using multi-temporal cloud masking algorithm in tropical environmental. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLI-B2: 95–100 [DOI: 10.5194/isprs-archives-XLI-B2-95-2016]

Chander G, Markham B L and Helder D L. 2009. Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote Sensing of Environment, 113(5): 893–903

Chen S L, Chen X H, Chen J, Jia P F, Cao X and Liu C Y. 2016. An iterative haze optimized transformation for automatic cloud/haze detection of landsat imagery. IEEE Transactions on Geoscience and Remote Sensing, 54(5): 2682–2694

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

Hagolle O, Huc M, Pascual D V and Dedieu G. 2010. A multi-temporal method for cloud detection, applied to FORMOSAT-2, VENμS, LANDSAT and SENTINEL-2 images. RemoteSensing of Environment, 114(8): 1747–1755

Hagolle O, Huc M, Pascual D V and Dedieu G. 2015. A multi-temporal and multi-spectral method to estimate aerosol optical thickness over land, for the atmospheric correction of formosat-2, landsat, VENμS and sentinel-2 images. Remote Sensing, 7(3): 2668–2691

Hall D K, Riggs G A and Salomonson V V. 1995. Development of methods for mapping global snow cover using moderate resolution imaging spectroradiometer data. Remote Sensing of Environment, 54(2): 127–140

Hu Y, Liu L Y, Liu L L, Peng D L, Jiao Q J and Zhang H. 2014. A landsat-5 atmospheric correction based on MODIS atmosphere products and 6S model. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(5): 1609–1615

Irish R R. 2000. Landsat 7 automatic cloud cover assessment//Proceedings of SPIE Volume 4049, Algorithms for Multispectral, Hyperspectral, and Ultraspectral Imagery. Orlando, FL: SPIE: 348–355 [DOI: 10.1117/12.410358]

Irish R R, Barker J L, Goward S N and Arvidson T. 2006. Characterization of the landsat-7 ETM+automated cloud-cover assessment (ACCA) algorithm. Photogrammetric Engineering and Remote Sensing, 72(10): 1179–1188

Jin S M, Homer C, Yang L M, Xian G, Fry J, Danielson P and Townsend P A. 2013. Automated cloud and shadow detection and filling using two-date Landsat imagery in the USA. International Journal of Remote Sensing, 34(5): 1540–1560

Ju J C and Roy D P. 2008. The availability of cloud-free Landsat ETM+data over the conterminous United States and globally. Remote Sensing of Environment, 112(3): 1196–1211

Kennedy R, Cohen W B, Schroeder T A. 2007. Trajectory-based change detection for automated characterization of forest disturbance dynamics. Remote Sensing of Environment, 110(3): 370–386

Lavanant L, Marguinaud P, Harang L, Lelay J, Sonia P and Philippe S. 2007. Operational cloud masking for the O&SISAF global METOP SST production. Proceedings of the Joint 2007 EUMETSAT Meteorological Satellite Conference and the 15th Satellite Meteorology and Oceanography Conference of the American Meteorological Society. Amsterdam, The Netherlands

Lee D S, Storey J C, Choate M J and Hayes R W. 2004. Four years of Landsat-7 on-orbit geometric calibration and performance. IEEE Transactions on Geoscience and Remote Sensing, 42(12): 2786–2795

Li Z W, Shen H F, Li H F, Xia G S, Gamba P and Zhang L P. 2017. Multi-feature combined cloud and cloud shadow detection in gaofen-1 wide field of view imagery. Remote Sensing of Environment, 191: 342–358

Mei L L, Vountas M, Gómez-Chova L, Rozanov V, Jäger M, Lotz W, Burrows J P and Hollmann R. 2017. A cloud masking algorithm for the xbaer aerosol retrieval using meris data. Remote Sensing of Environment, 197: 141–160

Rouse Jr J W, Haas R H, Schell J A and Deering D W. 1974. Monitoring vegetation systems in the great plains with erts//Proceedings of the 3rd Earth Resources Technology Satellite-1 Symposium-Volume I: Technical Presentations. Washington: NASA Special Publication: 309–317

Roy D P, Ju J C, Kline K, Scaramuzza P L, Kovalskyy V, Hansen M, Loveland T R, Vermote E and Zhang C S. 2010. Web-enabled Landsat Data (WELD): landsat ETM+ composited mosaics of the conterminous United States. Remote Sensing of Environment, 114(1): 35–49

Saunders R W and Kriebel K T. 1998. An improved method for detecting clear sky and cloudy radiances from AVHRR data. International Journal of Remote Sensing, 9(1): 123–150

USGS. 2016a. Landsat 8 OLI and TIRS calibration notices[EB/OL]. [2018-02-06].https://landsat.usgs.gov/landsat-8-l8-data-usershandbookhttps://landsat.usgs.gov/landsat-8-l8-data-usershandbook

USGS. 2016b. Landsat 8 (L8) data users handbook[EB/OL].https://landsat.usgs.gov/landsat-8-l8-operational-land-imager-oli-and-thermal-infrared-sensor-tirshttp://landsat.usgs.gov/landsat-8-l8-operational-land-imager-oli-and-thermal-infrared-sensor-tirs

Vermote E, Justice C, Claverie M and Frabch B. 2016. Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product. Remote Sensing of Environment, 185: 46–56

Woodcock C E and Strahler A H. 1987. The factor of scale in remote sensing. Remote Sensing of Environment, 21(3): 311–332

Zhang Y, Guindon B and Cihlar J. 2002. An image transform to characterize and compensate for spatial variations in thin cloud contamination of Landsat images. Remote Sensing of Environment, 82(2-3): 173–187

Zhang Y C, Rossow W B, Lacis A A, Oinas V and Mishchenko M I. 2004. Calculation of radiative fluxes from the surface to top of atmosphere based on ISCCP and other global data sets: refinements of the radiative transfer model and the input data. Journal of Geophysical Research: Atmospheres, 109(D19): D19105

Zhu Z, Wang S X and Woodcock C E. 2015. Improvement and expansion of the Fmask algorithm: cloud, cloud shadow, and snow detection for Landsats 4-7, 8, and Sentinel 2 images. Remote Sensing and Environment, 159: 269–277

Zhu Z and Woodcock C E. 2012. Object-based cloud and cloud shadow detection in Landsat imagery. Remote Sensing of Environment, 118: 83–94

Zhu Z and Woodcock C E. 2014. Automated cloud, cloud shadow, and snow detection in multitemporal Landsat data: an algorithm designed specifically for monitoring land cover change. Remote Sensing of Environment, 152: 217–234

Alert me when the article has been cited

提交

Retrieval and validation of sea surface temperature from HY-1D COCTS

Research on multispectral satellite image cloud and cloud shadow detection algorithm of domestic satellite

Research on cloud detection for HY-1C CZI remote sensing images collected over lands

Cloud detection for FY-3D MERSI Ⅱ images combine radiative transfer simulation and shallow neural network

Improvement of unified sample cloud detection technology and its application in GF-6 WFV cloud detection