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 
  
Figure 2. Spot5-HRS times series. Illustration for the Hispaniola test area € CNES 
Contrary to airborne sensors and as illustrated in Figure 1, it is 
difficult to acquire satellite images without any cloud, 
especially in areas such as the French overseas territories (e.g. 
Guyane, Martinique, Guadeloupe) where the cloud cover is 
known to be important all year round. To limit this problem, 
providers of satellite data generally wait for a favourable 
weather window so that the cloud cover is minimal. However, 
this solution is not operable in areas (e.g. Guyane) that are 
almost always nebulous. Here, the solution, already mentioned 
above, consists in acquiring images regardless the weather 
conditions. That results in obtaining time series i.e. a pile of 
satellite images with a high number of images, as depicted in 
Figure 2 for our Hispaniola test area. If this solution involves 
difficult pos-procedures for generating a virtual cloud-free 
satellite image from those contained in time series, it also 
presents the advantage of maximizing the acquisition capability 
of the satellite. This is the main reason why this acquisition 
configuration is considered by an increasing number of satellite 
data providers. In our project, we assumed to have such multi- 
temporal satellite images and we built our cloud detection 
system upon this hypothesis. 
Until now, the analysis of satellite images for cloud 
identification and removal has been carried out manually. That 
entails a long, fastidious and costly work. As a consequence, it 
becomes necessary to introduce a certain degree of automation 
in the cloud detection procedure i.e. to develop expert systems 
that are able to focus the operator's attention on potential cloud 
areas. 
1.1 Related works 
In the last few years, many researches have been carried out to 
automate the cloud detection in satellite images. 
Some works can be found in the meteorology community, for 
example in the paper by Derrien and Le Gléau (2010) who 
propose to combine a temporal analysis to a region-growing 
technique in order to improve the detection of low clouds at 
twilight. If this work appears to be interesting, the meteorology 
context appears to be very different from the context generally 
considered in the geoscience community. Thus, the images used 
542 
in the above-mentioned project are delivered by the SEVIRI 
system that offers 11 spectral bands (including thermal bands), a 
high temporal resolution (one image every 15 minutes) but a 
low spatial resolution (3km at nadir). By contrast, the images 
used in the geoscience community generally offer fewer spectral 
channels but a better spatial resolution, with a Ground Sample 
Distance (GSD) of e.g. 5m for SPOTS-HRS, 70cm for Pléiades 
and 50cm for Worldview. 
Most of the cloud detection approaches found in the geoscience 
literature are dedicated to single-date satellite images (Irish, 
2000) (Irish et al, 2006) (Le Hégarat and André, 2009). By 
contrast, only a few of them make a full use of multi-temporal 
satellite images. In this category, the method by Sedano et al. 
(2011), originally designed for single-date High-Resolution 
images, uses information from multi-temporal Low-Resolution 
MODIS images in order to perform the cloud detection. Thus, 
the difference between MODIS and input images is analysed to 
extract the seed points that correspond to clouds, which are then 
used in a subsequent region-growing procedure in order to 
delineate clouds finely. The main drawback of the method is 
related to the low resolution of MODIS images (featured by a 
GSD of 250m) that makes the detection of small clouds 
impossible. Multi-temporal LANDSAT and FORMOSAT-2 
images are used in (Hagolle et al., 2010). The extraction of 
cloud pixels is here based on the assumption that a sudden 
increase of reflectance in the blue band between two images is 
due to the presence of a cloud. This leads to the computation of 
a preliminary cloud mask that is then refined using additional 
radiometric tests, performed on the remaining bands. If this 
method is shown to give good results, it is limited to the 
availability of several bands and can also not be used in every 
context, especially when using panchromatic SPOTS-HRS 
images. 
1.2 Presentation 
The main goal of this paper is to present the method developed 
at the French Mapping Agency for identifying and delineating 
clouds in High Resolution multi-temporal satellite images. We 
here propose to couple the ideas of the papers by Sedano et al. 
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