Orthophoto 7) 
  
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 
extraction step (Section 3.2) and the region-growing step 
(Section 3.3). 
3.2 Seed extraction 
This first step also aims at achieving the following goal: having 
one seed point (at least) for each cloud. This step is based on the 
two following hypotheses (Hagolle et al., 2010): 
* A significant increase of reflectance between two 
orthophotos of the pile (time series) is related to the 
presence of a cloud 
* Clouds are not located at the same place in two 
different orthophotos (they are moving!) 
Reference 
Orthophoto = 
RO 4x \ 
   
  
pixel-to-nixel, 
comparison, 
Í 
j 
j 
1 
C <7 
Figure 4. Seed Extraction. Illustration of the pixel-to-pixel 
comparison between the Reference Orthophoto RO and the 
other orthophotos PO contained in the pile (time series) 
  
PO 
  
To extract cloud seeds in one orthophoto, we proceed in the 
following way. Firstly, we take one orthophoto as a reference 
(this Reference Orthophoto RO corresponds to the orthophoto 
we want to detect clouds in). Secondly, we select the other 
orthophotos of the pile that intersect this Reference Orthophoto 
RO and we crop them according to their overlapping on the 
Reference Orthophoto (Note that these cropped orthophotos are 
called Pile Orthophotos PO in the rest of this paper). From 
there, a pixel-to-pixel analysis is carried out between the 
Reference Orthophoto RO and each PO (Figure 4). If the 
radiometric difference is better than a given threshold Tg (in our 
project, 8 bits images are used and Tp=170), the pixel of the 
Reference Orthophoto receives a positive vote. Eventually (after 
this process is carried out for each PO), a pixel of RO is labelled 
“cloud” if a 2/3 majority votes for it. 
At the end of this step, pixels corresponding to clouds are also 
identified. However, some cloud pixels are missing. This is the 
case for example for pixels covered with a cloud in all the 
orthophotos of the pile (in that case, the pixel-to-pixel 
difference is never higher than the Ty threshold). This issue is 
solved by operating a region-growing procedure, starting from 
these seed points, as detailed in the next section. 
3.3 Region growing 
This step also aims at delineating clouds finely. It is based on 
the two following hypotheses, found e.g. in (Le Hégarat and 
André, 2009): 
544 
* (Clouds are brighter than the underlying landscape 
* Clouds are connex objects 
The region-growing algorithm used in our system is based on 
two criteria: a radiometric criterion and a homogeneity criterion. 
To be aggregated to a cloud region, the radiometry of the pixel 
must also lie within a range of given values (in our project, 8 
bits images are used and range - [Tana ; 255] with Trg=200). In 
addition, all the neighbours of this pixel must satisfy this first 
criterion. Note that the size of the neighbourhood structuring 
element Sy can be specified to the algorithm and is set by 
default to Sy=1. Some results, obtained with panchromatic 
satellite images, are presented in Figures 5 and 6. 
4. RESULTS AND DISCUSSION 
The method was tested in the Hispaniola (including the 
Dominican Republic and Haiti) test site that contains 52 
SPOTS-HRS images, with an average overlapping of 6 images. 
In this paper, we present the preliminary outcomes delivered by 
our method for a pile of 9 images. These results were evaluated 
visually and are discussed in this section. 
As can be seen in Figures 5 and 6, we observe that most of 
clouds are correctly detected by our method. In particular, the 
method is effective in detecting cumulus clouds that appear to 
be fleecy in the scene. This is due to the fact that these clouds 
have sharp outlines and are brighter than the surrounding 
(ground) objects. In that sense, they perfectly fit the two 
assumptions used in the region-growing step: this explains why 
our procedure is so successful. 
By contrast, in a similar way as the other methods found in 
literature (Sedano et al., 2011), the detection of mists appears to 
be more difficult. This is due to the fact that these clouds are 
generally thinner and less bright than e.g. cumulus clouds. 
Then, the spectral variation of the corresponding pixels is not 
significantly high (it lies in the normal range) and the first 
assumption used for extracting seeds is not verified. 
In addition, we notice that confusions appear with some bright 
objects like white sand beaches. This is related to the fact that 
the corresponding regions in input SPOTS-HRS images may be 
saturated because of the poor radiometric resolution of input 
images. As a consequence, these regions are wrongly (and 
systematically) alerted as clouds at the end of the second step. 
To remove them, a spatial analysis is necessary. It consists in 
comparing the number of pixels of the resulting region 
(computed at the end of the region-growing procedure) and the 
number of initial seeds. Our experiments show that, in the case 
of a saturated region, this ratio is high. By contrast, in the case 
of a cloud, this ratio is always low. This criterion is also used 
(with a threshold Ts=6, applied to this ratio) to remove 
misclassified regions from the outcomes. 
5. PERSPECTIVES 
In a near future, we plan to test our cloud detection method with 
multi-spectral Pléiades images. For that purpose, and because 
our method is designed for images with one band, we will use 
the panchromatic channel of these images. The major 
drawbacks - encountered and presented in the previous section - 
should not appear in these new experiments. Thus, regarding the 
errors related to saturated regions in input data, they should be 
discarded because 12-bits Pléiades images are shown to have a 
(much) higher dynamic range than the SPOTS-HRS images. In 
Intern: 
  
Figure 5. 
(bottom) 1