Automatic Cloud and Shadow Detection in Optical Satellite Imagery Without Using Thermal Bands-Application to Suomi NPP VIIRS Images over Fennoscandia

• Published in: Remote Sensing
• Main Authors: Parmes, Eija, Rauste, Yrjö, Molinier, Matthieu, Andersson, Kaj, Seitsonen, Lauri
• Format: Article in Journal/Newspaper
• Language: English
• Published: 2017
• Subjects: cloud and shadow masking; optical satellite images; Suomi NPP VIIRS; Sentinel-2; surface reflectance; rule-based classification; Fennoscandia
• Online Access: https://cris.vtt.fi/en/publications/cc51ab93-e597-4e74-bfb8-5efb095022c0; https://doi.org/10.3390/rs9080806; http://www.scopus.com/inward/record.url?scp=85028319233&partnerID=8YFLogxK

In land monitoring applications, clouds and shadows are considered noise that should be removed as automatically and quickly as possible, before further analysis. This paper presents a method to detect clouds and shadows in Suomi NPP satellite's VIIRS (Visible Infrared Imaging Radiometer Suite) satellite images. The proposed cloud and shadow detection method has two distinct features when compared to many other methods. First, the method does not use the thermal bands and can thus be applied to other sensors which do not contain thermal channels, such as Sentinel-2 data. Secondly, the method uses the ratio between blue and green reflectance to detect shadows. Seven hundred and forty-seven VIIRS images over Fennoscandia from August 2014 to April 2016 were processed to train and develop the method. Twenty four points from every tenth of the images were used in accuracy assessment. These 1752 points were interpreted visually to cloud, cloud shadow and clear classes, then compared to the output of the cloud and shadow detection. The comparison on VIIRS images showed 94.2% correct detection rates and 11.1% false alarms for clouds, and respectively 36.1% and 82.7% for shadows. The results on cloud detection were similar to state-of-the-art methods. Shadows showed correctly on the northern edge of the clouds, but many shadows were wrongly assigned to other classes in some cases (e.g., to water class on lake and forest boundary, or with shadows over cloud). This may be due to the low spatial resolution of VIIRS images, where shadows are only a few pixels wide and contain lots of mixed pixels.