ALPMON FINAL REPORT - ARC systems research

Contract ENV4-CT96-0359 ALPMON
information usually available about the atmosphere. Having different measures of the horizontal
visibility within the research area at a time before or after satellite image acquisition makes
estimation of a mean value necessary. The result is not very accurate and may cause overcorrection
or under-correction. Over-correction, additionally, results in critical loss of information in
dark areas of the image.
Thus, the result of atmospheric correction is to be seen as very critical. Resulting changes appear to
be not due to real correction but to errors in the correction models. The shortcomings of the available
atmospheric correction algorithms are in our opinion according to the dramatic height differences in all
of the test sites. These are not considered properly in the correction models. In this direction efforts
have to be addressed to the scientific community dealing with atmospheric effects on satellite data
especially in alpine terrain.
Further discussion concerned the order of performing atmospheric correction before or after
topographic normalisation. In general, atmospheric correction is performed on original data, followed
by topographic normalisation. As atmospheric correction may cause information loss mainly within the
lower grey value ranges this becomes a problem for topographic normalisation. This is due to the fact
that diffuse illumination is important for atmospheric correction as well as for topographic
normalisation. Therefore, the opposite order was tested as well. Not being able to solve this problem
by performing the correction in two separate steps, an integrated approach for both correction tasks,
as proposed by Sandmeier and Itten (1997), is strongly recommended. Unfortunately, no such
software is available to the public up to now.
In summary, one can say that from visual interpretation application of the described atmospheric
correction algorithms in general cannot be recommended (also compare section 3.4.2).
3.3.6 Data preparation
3.3.6.1 Data merging
Current remote sensors offer a wide variety of image data with different characteristics in terms of
temporal, geometric, radiometric and spectral resolution. Although the information content of these
images might be partially overlapping, the complementary aspects represent a valuable improvement
for information extraction. To exploit the entire content of multi-sensor image data, appropriate
techniques for image fusion are indispensable. The adaptive image fusion (AIF) method allows the
fusion of geometric (spatial) and thematic (spectral) features from multi-source raster data using
adaptive filter algorithms. If applied to multi-resolution image data, it will sharpen the low spatial
resolution image according to object edges found in the higher spatial resolution image. In contrast to
substitution methods, such as Intensity-Hue-Saturation or Principal-Component Merging, AIF
preserves the spectral characteristics of the original low resolution image.
A major prerequisite for successful data fusion is the geometric and temporal accordance of the input
images. A geometric displacement will lead to undesired artefacts, independent from the fusion
method applied. Differences in the time of data acquisition are another source of artefacts because of
changes that might occur between the two dates. However, these changes are less critical when
applying AIF, as long as they only reflect changes in the spectral characteristic but not in the size of
objects.
Assuming no geometric or temporal variations between the images the quality of the fusion process
depends on the information content of the images. Low quality of the panchromatic data might lead to
poor results because the fusion algorithm will not be able to detect the features necessary for the
merging procedure. However, applying substitution techniques to such a low quality image will not
significantly improve the results either. The major limitation of AIF lies in the loss of local texture that is
present in the panchromatic image. This local variation of grey values cannot be reconstructed in the
multi-spectral image without distorting its spectral characteristic. Only edges that appear clearly in both
images will be sharpened.
The conclusions drawn from the application during the project are manifold. The AIF is considered
useful for areas, that are dominated by objects larger than the low resolution pixel size. Taking the
average pixel size of today’s multi-spectral sensors (20 – 30m) this prerequisite is only true for certain
types of image objects. Due to the size and heterogeneity of the single objects, this does not hold for
forest areas. There the AIF leads to a loss of details caused by averaging the multi-spectral pixels,
which is due to missing information in panchromatic image. However, for the segmentation of forest
and non forest areas the approach is considered valuable. One suggestion might be to use the fusion
JR, RSDE, ALU, LMU, Seibersdorf, WSL 77