B3, 2012 
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B3, 2012 
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia 
AUTOMATIC CLOUD DETECTION FROM MULTI-TEMPORAL SATELLITE IMAGES: 
TOWARDS THE USE OF PLEIADES TIME SERIES 
Nicolas Champion 
IGN-F/EIDS, Ramonville Saint-Agne, France — nicolas.champion@ign.fr 
WGs from TCs III and I: Pléiades System Applications 
KEY WORDS: Cloud detection, Multi-temporal satellite images, SPOTS-HRS, Pléiades, Region-growing 
ABSTRACT: 
Contrary to aerial images, satellite images are often affected by the presence of clouds. Identifying and removing these clouds is one 
of the primary steps to perform when processing satellite images, as they may alter subsequent procedures such as atmospheric 
corrections, DSM production or land cover classification. The main goal of this paper is to present the cloud detection approach, 
developed at the French Mapping agency. Our approach is based on the availability of multi-temporal satellite images (i.e. time 
series that generally contain between 5 and 10 images) and is based on a region-growing procedure. Seeds (corresponding to clouds) 
are firstly extracted through a pixel-to-pixel comparison between the images contained in time series (the presence of a cloud is here 
assumed to be related to a high variation of reflectance between two images). Clouds are then delineated finely using a dedicated 
region-growing algorithm. The method, originally designed for panchromatic SPOTS-HRS images, is tested in this paper using time 
series with 9 multi-temporal satellite images. Our preliminary experiments show the good performances of our method. In a near 
future, the method will be applied to Pléiades images, acquired during the in-flight commissioning phase of the satellite (launched at 
the end of 2011). In that context, this is a particular goal of this paper to show to which extent and in which way our method can be 
adapted to this kind of imagery. 
1. INTRODUCTION 
Detecting clouds is one of the primary steps to perform when 
processing satellite images. In an industrial context, more 
particularly in the context of the French mapping agency (IGN- 
F), clouds may affect production lines in three different 
manners: 
* During the acquisition of satellite data. At the 
wavelengths of the visible light, clouds are opaque and also 
hide the ground surface from Earth observation satellites. 
As a work-around, it is possible to acquire a scene in 
several passes: that results in time series containing a high 
number of satellite images (in general between 5 and 10) 
acquired at different epochs. 
* During the computation of DSM. The presence of 
clouds in satellite stereoviews is a classical cause of failure 
of stereo-matching procedures (Grün, 2000) (Eckert et al., 
2005) 
* During the computation of large cloud-free 
orthomosaics from input satellite images. The presence of 
clouds in input satellite images has here several 
consequences. As mentioned in the first item, that implies 
the acquisition of time series i.e. the acquisition of multi- 
temporal satellite images. Therefore, computing large 
seamless orthomosaics from these multi-temporal images 
involves identifying cloud pixels in input data so that only 
cloud-free pixels are used for generating the final product 
(Din-Chang et al., 2008). In addition, due to the fact that 
images are not acquired simultaneously, there are 
radiometric heterogeneities between them. These 
differences may be related to variations in the Bidirectional 
Reflectance Distribution Function (BRDF) of ground 
Figure 1. The first images acquired 
highlight one of the primary problems that satellite acquisition 
generally copes with: the presence of clouds in the scene. A 
surfaces or to variations in the solar illumination. These 
differences must also be considered when generating 
orthomosaics through specific radiometric equalization 
procedures (Chandelier and Martinoty, 2009) (Falala et al., 
2008). Eventually, the cloud shadows may cause some 
problems in the procedure, more particularly during the 
radiometric equalization. In particular, they may cause a 
“leopard skin” effect in the final orthomosaics (Soille, 
2008). As a consequence, these shadows must also be 
detected for a subsequent de-shadowing procedure that 
aims at enhancing the display of the corresponding areas in 
the final orthomosaics (Simpson and Stitt, 1998) (Richter 
and Miiller, 2005). 
      
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y the Pléiades system 
view over the historical part of Paris (2011/12/22). 
O CNES