International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B3. Istanbul 2004
registered cloudy images, and to mosaic “clean” data together.
If the clean data pixels from different scenes are simply
consolidated without additional processing, the final image will
be very "speckled" and appear discontinuous. Therefore, a
patch of pixels rather than the individual pixels is chosen to
form the final mosaic.
The final mosaic is composed from the images with cloud,
cloud-shadow masks and the merged image generated from the
merging of sub-images procedure. To suppress the visibility of
the seam line between adjacent patches, the residual intensity
differences between the patches are balanced using the intensity
histograms of local patches. Secondly, the patches are made to
overlap at their boundaries and the system will blend the image
intensity from one patch to another in these overlapping
regions. Finally, the images resulting from the mosaic process
are geo-referenced to a map. The mosaic production procedure
will put the image from the mosaic process into the map.
3. RESULTS AND CONCLUSIONS
An example of applying the cloud-free mosaicking algorithm
the six cloudy SPOT panchromatic images is shown in Figure 2.
Figure 3-(a) shows a mosaic of cloudy SPOT multispectral
images over Singapore and the southern part of the Peninsular
Malaysia. The resulting cloud-free and cloud shadow-free
mosaic is shown in Figure 3-(b). The mosaicking algorithm has
also been tested on 1-m resolution IKONOS colour images.
In this paper, we have presented the method for producing
cloud-free and cloud shadow-free multi-scene mosaics from
cloudy SPOT and IKONOS images. The system has been
implemented successfully over a large area covered by about 50
SPOT scenes. The success of the cloud-free and cloud shadow-
free mosaic depends on the choice of the shadow, vegetation
and cloud intensity thresholds. Confusions arise when high-
albedo open land surfaces or buildings are encountered. Such
756
confusions can be resolved by making use of size and colour
information to classify the pixels /patches into a few broad land
cover classes. In many cases the clouds that need to be masked
out are much large than the individual building, an automatic
method is developed to calculate the size of the bright patches
in order to eliminate improper masking of these buildings. As a
result, this procedure allows a few small cloud patches to
remain in the mosaic. A large, very bright and white patch of
open land surface will be considered as cloud. When the bright
and white patch of open land does not contain cloud-shadow, it
is still possible for this patch of open land surface to be selected
and used in forming the final mosaic. The approximate location
of cloud shadow can be predicted based on the knowledge of
solar illumination direction, sensor viewing direction and cloud
height.
References:
S. C. Liew, M. Li, L.K. Kwoh, P. Chen, and H. Lim, “Cloud-
free multi-scene mosaics of SPOT images,” in Proc.
International Geoscience and Remote Sensing Symposium,
1998, vol. 2, pp. 1083-1085
M. Li, S. C. Liew, L.K. Kwoh, and H. Lim, “Improved cloud-
free multi-scene mosaics of SPOT images,” in Proc. Asian
Conf. Remote Sensing, 1999, vol. 1, pp. 294-298
M. Li, S. C. Liew, and L.K. Kwoh, “Generating “cloud free”
and "cloud-shadow free” mosaic for SPOT panchromatic
images,” in Proc. International Geoscience and Remote Sensing
Symposium, 2002, vol. 4, pp. 2480-2482
M. Li, S. C. Liew, and L.K. Kwoh, “Producing Cloud Free and
Cloud-Shadow Free Mosaic from Cloudy IKONOS Images” in
Proc. International ~~ Geoscience and Remote | ensing
Symposium, 2003, vol. 6, pp. 3946-3948
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