TY - JOUR

T1 - Cloud detection based on high resolution stereo pairs of the geostationary meteosat images

AU - Dehnavi, Sahar

AU - Maghsoudi, Yasser

AU - Zakšek , Klemen

AU - Valadan Zoej, Mohammad Javad

AU - Seckmeyer, Gunther

AU - Skripachev , Vladimir

N1 - Funding Information: The publication of this article was funded by the Open Access Fund of Leibniz Universität Hannover. We acknowledge EUMETSAT for the provision of SEVIRI data via EUMETCast. We also would like to thank Heipke (from Leibniz University of Hannover) for his comments and personal discussion with him, and Mirzade (from Shanghai University of China) for his help during testing some routines. We say thanks to the Technische Informations Bibliothek (TIB), the central library of Leibniz University, since the publication of this article was funded by the Open Access Fund of Leibniz Universität Hannover. We also greatly appreciate the reviewers for their complimentary comments and suggestions, which allowed us to greatly improve the quality of this research article.

PY - 2020/1/23

Y1 - 2020/1/23

N2 - Due to the considerable impact of clouds on the energy balance in the atmosphere and on the earth surface, they are of great importance for various applications in meteorology or remote sensing. An important aspect of the cloud research studies is the detection of cloudy pixels from the processing of satellite images. In this research, we investigated a stereographic method on a new set of Meteosat images, namely the combination of the high resolution visible (HRV) channel of the Meteosat-8 Indian Ocean Data Coverage (IODC) as a stereo pair with the HRV channel of the Meteosat Second Generation (MSG) Meteosat-10 image at 0° E. In addition, an approach based on the outputs from stereo analysis was proposed to detect cloudy pixels. This approach is introduced with a 2D-scatterplot based on the parallax value and the minimum intersection distance. The mentioned scatterplot was applied to determine/detect cloudy pixels in various image subsets with different amounts of cloud cover. Apart from the general advantage of the applied stereography method, which only depends on geometric relationships, the cloud detection results are also improved because: (1) The stereo pair is the HRV bands of the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) sensor, with the highest spatial resolution available from the Meteosat geostationary platform; and (2) the time difference between the image pairs is nearly 5 s, which improves the matching results and also decreases the effect of cloud movements. In order to prove this improvement, the results of this stereo-based approach were compared with three different reflectance-based target detection techniques, including the adaptive coherent estimator (ACE), constrained energy minimization (CEM), and matched filter (MF). The comparison of the receiver operating characteristics (ROC) detection curves and the area under these curves (AUC) showed better detection results with the proposed method. The AUC value was 0.79, 0.90, 0.90, and 0.93 respectively for ACE, CEM, MF, and the proposed stereo-based detection approach. The results of this research shall enable a more realistic modelling of down-welling solar irradiance in the future.

AB - Due to the considerable impact of clouds on the energy balance in the atmosphere and on the earth surface, they are of great importance for various applications in meteorology or remote sensing. An important aspect of the cloud research studies is the detection of cloudy pixels from the processing of satellite images. In this research, we investigated a stereographic method on a new set of Meteosat images, namely the combination of the high resolution visible (HRV) channel of the Meteosat-8 Indian Ocean Data Coverage (IODC) as a stereo pair with the HRV channel of the Meteosat Second Generation (MSG) Meteosat-10 image at 0° E. In addition, an approach based on the outputs from stereo analysis was proposed to detect cloudy pixels. This approach is introduced with a 2D-scatterplot based on the parallax value and the minimum intersection distance. The mentioned scatterplot was applied to determine/detect cloudy pixels in various image subsets with different amounts of cloud cover. Apart from the general advantage of the applied stereography method, which only depends on geometric relationships, the cloud detection results are also improved because: (1) The stereo pair is the HRV bands of the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) sensor, with the highest spatial resolution available from the Meteosat geostationary platform; and (2) the time difference between the image pairs is nearly 5 s, which improves the matching results and also decreases the effect of cloud movements. In order to prove this improvement, the results of this stereo-based approach were compared with three different reflectance-based target detection techniques, including the adaptive coherent estimator (ACE), constrained energy minimization (CEM), and matched filter (MF). The comparison of the receiver operating characteristics (ROC) detection curves and the area under these curves (AUC) showed better detection results with the proposed method. The AUC value was 0.79, 0.90, 0.90, and 0.93 respectively for ACE, CEM, MF, and the proposed stereo-based detection approach. The results of this research shall enable a more realistic modelling of down-welling solar irradiance in the future.

KW - Cloud detection

KW - Geostationary satellites

KW - Photogrammetry and remote sensing

KW - Stereography

UR - http://www.scopus.com/inward/record.url?scp=85080907386&partnerID=8YFLogxK

U2 - 10.3390/rs12030371

DO - 10.3390/rs12030371

M3 - Article

VL - 12

JO - Remote Sensing

JF - Remote Sensing

SN - 2072-4292

IS - 3

M1 - 371

ER -