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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
(2011) and by Hagolle et al. (2010) to build our cloud detection
system. Following the design of the method by Sedano et al.
(2011), our method is composed of two steps: seeds are firstly
extracted and secondly extended during a region-growing
procedure in order to delineate clouds finely. However, our
method differs from this latter method in the way to extract
seeds. We here follow the assumption made by Hagolle et al.
(2010) and we consider that a high variation of reflectance
between two images (in time series) is related to the presence of
a cloud in the scene.
Our method was originally designed for SPOT5-HRS images,
mainly because these images are still used in our production
lines, in particular for the production of the Reference3D®
database (Bouillon et al., 2006). However, with the advent of
High-Resolution satellite images such as Geoeye, Worldview
and Pléiades, it becomes necessary to adapt (at least, to test) the
existing procedures to these new sensors. With this objective in
mind, IGN-F is taking part in 2012 in the user's thematic
commissioning that is conducted by the French Space Agency
(CNES) and that aims at validating the future products and
services based on Pléiades. More particularly, we plan to use
Pléiades images to replay some studies, already carried out
during the ORFEO! accompaniment program. These
experiments will concern:
* 2D change detection (Champion et al., 2010) (Le-
Bris and Chehata, 2011) for updating building databases
* The update of Land Cover / Land Use databases
(Hermosilla et al., 2011)
* 3D change detection by comparison of Digital
Surface Models, computed from satellite images acquired
at two different dates (Guérin et al., 2012)
* The 3D reconstruction of buildings (Durupt and
Taillandier, 2006) (Lafarge et al., 2008)
* The production of large area seamless orthomosaics
(Falala et al., 2008)
* Cloud detection
In that context, this is also a particular goal of this paper to
show to which extent and in which way the method presented
here can be adapted to Pleiades images.
The remaining of the paper is organized as follows. Section 2
presents input data. Section 3 presents our cloud detection
method. Section 4 shows the preliminary results that we
obtained, starting from SPOTS-HRS images. Eventually,
Section 5 highlights the perspectives related to the use of
Pleiades time series for detecting clouds.
2. INPUT DATA
Two different kinds of input data are also considered in our
project: panchromatic SPOTS-HRS images and (in a near
future) Pléiades images.
SPOTS-HRS data. As shown in Figure 3 and as detailed in
(Bouillon et al., 2006), the SPOT5-HRS instrument is composed
of two telescopes with a viewing angle (along the track) of 20°
forward and 20° aft. This configuration allows the acquisition of
single pass stereopairs, with a time delay between 2 images of
90 seconds and a corresponding Base-to-Height (B/H) ratio of
about 0.8. The system is featured by a swath of 120 km, a pixel
GSD of 5 m along the track and 10 m across the track. For our
| http//smse.cnes. fr/PLEIADES/index.htm. | Last visited:
2012/4/16
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project, multi-temporal panchromatic images were acquired.
That resulted in the availability of time series i.e. a pile of
images, acquired at different epochs, with an average
overlapping of 6 images, as depicted in Figure 2.
Ta
To + 90 seconds
dw
Rear HRS
max. 600 km
Figure 3. Acquisition configuration of the SPOTS-HRS
instrument (Bouillon et al, 2006)
Pléiades data. Contrary to SPOTS-HRS images, Pléiades
images are not only panchromatic but have 4 channels: Red,
Green, Blue and Near Infrared (NIR). In addition, they are
featured by a ground pixel of 70cm. In our project, we also
assume to have time series, in a similar way as for SPOT5-HRS
data.
3. METHOD
As introduced in Section 1.2 and as justified and detailed in this
section, our method is composed of 3 steps:
* A pre-processing step
* A seed extraction step
* A region growing step
3.1 Pre-processing
The pre-processing step aims at making the subsequent
analysis/comparison easier. It consists on the one hand in a
geometric correction and on the other hand in a radiometric
correction.
Geometric pre-processing. Because our method is based on a
pixel-to-pixel comparison between the several satellite images
contained in the time series (See Section 3.2 for more details),
input satellite images must be co-registered. In our project,
input satellite images are orthorectified using an in-house DTM
(Reference3D®). Note that we only use (in the rest of the
algorithm) the orthophotos generated at this step. It should be
noted here that the method did not appear to be sensitive to the
accuracy of the DTM used for orthorectification. Thus, the
outcomes produced with orthophotos computed with GDEM
(Global Digital Elevation Model, derived from ASTER images
through stereo-matching algorithms) are not significantly
different from the outcomes based on the Reference3D or
SRTM (Shuttle Radar Topography Mission) DTMs, even
though these two latter DTMs are known to have a better
accuracy (in altimetry) than GDEM.
Radiometric pre-processing. In addition to this geometric
correction and because the comparison (detailed in Section 3.2)
is made on a radiometrical basis, input data must be
radiometrically corrected. For that purpose, we followed the
recommendations found in (Lillesand et al., 2008)
In the two following sections, we propose to describe the two
steps involved in our cloud detection approach: the seed
