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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 5. (top) Initial panchromatic SPOTS-HRS orthophoto. 
(bottom) The outcomes of our cloud detection algorithm. Pixels 
labelled *clouds" are highlighted in blue. 
545 
  
   
Figure 6. Zoom on the area highlighted in red in Figure 5. 
(top) Initial images. (bottom) Clouds detected by the method 
are highlighted in blue. 
addition, regarding the false negative detection related to mists, 
the information given by the NIR channel [750-950 nm] of 
Pléiades images might be very useful. Some tests are being 
made in that direction. 
Eventually, the detection of shadows, already mentioned in the 
introduction and that appears to be difficult to carry out with 
panchromatic SPOTS-HRS images, becomes possible with 
multi-spectral Pléiades images. In that context, a cloud shadow 
method is being considered. In a similar way as the cloud 
detection method presented in this paper, it is based on a region- 
growing algorithm. It however differs in the way to extract 
seeds. This step is here carried out by selecting the pixels of the 
Reference Orthophoto RO that appear to be “darker” than the 
corresponding pixels of the other orthophotos PO contained in 
the pile. We moreover use the information given by the NIR 
channel that appears to better discriminate shadow pixels 
(Richter and Mueller, 2005). The detection of shadows is all the 
more important as it enables - in a similar way as (Le Hégarat- 
Mascle and André, 2009) - a joint detection of clouds and 
corresponding shadows. In our framework, clouds and shadows 
will be detected separately. We will then compare their 
respective size and shape (given the incidence angle of the sun, 
the local terrain model and an approximate altitude for clouds), 
which should lead to a mutual validation (or invalidation) of 
their detection. 
6. CONCLUSION 
In this paper, we have presented a method for the automatic 
identification and delineation of clouds in High-Resolution 
satellite images. Our method, originally designed for 
panchromatic SPOTS-HRS multi-temporal images, is based on 
a region-growing algorithm. It is easy to implement and requires 
only 4 parameters (Tg, Trg, Sn, Ts) that can be easily set to 
default. As an advantage to other methods found in literature, 
our method does not require thermal bands and works on