Implementing a shadow detection algorithm for synthetic vision systems in reconfigurable hardware

2006 ◽  
Author(s):  
Jumoke Ladeji-Osias ◽  
Andre Theobalds ◽  
Otsebele Nare ◽  
Theirry Wandji ◽  
Craig Scott ◽  
...  
2018 ◽  
Vol 15 (10) ◽  
pp. 1610-1614
Author(s):  
Lin Sun ◽  
Quan Wang ◽  
Xueying Zhou ◽  
Jing Wei ◽  
Xu Yang ◽  
...  

2021 ◽  
Author(s):  
Victor Trees ◽  
Ping Wang ◽  
Piet Stammes ◽  
Lieuwe G. Tilstra ◽  
David P. Donovan ◽  
...  

Abstract. Cloud shadows are observed by the TROPOMI satellite instrument as a result of its high spatial resolution as compared to its predecessor instruments. These shadows contaminate TROPOMI's air quality measurements, because shadows are generally not taken into account in the models that are used for aerosol and trace gas retrievals. If the shadows are to be removed from the data, or if shadows are to be studied, an automatic detection of the shadow pixels is needed. We present the Detection AlgoRithm for CLOud Shadows (DARCLOS) for TROPOMI, which is the first cloud shadow detection algorithm for a spaceborne spectrometer. DARCLOS raises potential cloud shadow flags (PCSFs), and actual cloud shadow flags (ACSFs). The PCSFs indicate the TROPOMI ground pixels that are potentially affected by cloud shadows based on a geometric consideration with safety margins. The ACSFs are a refinement of the PCSFs using spectral reflectance information of the PCSF pixels, and identify the TROPOMI ground pixels that are confidently affected by cloud shadows. We validate DARCLOS with true color images made by the VIIRS instrument on board of Suomi NPP orbiting in close constellation with TROPOMI on board of Sentinel 5-P. We conclude that the PCSF can be used to exclude cloud shadow contamination from TROPOMI data, while the ACSF can be used to select pixels for the scientific analysis of cloud shadow effects.


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