ALPMON FINAL REPORT - ARC systems research

Contract ENV4-CT96-0359 ALPMON
as proposed by Sandmeier and Itten, 1997, is strongly recommended. Unfortunately, no such software
is available in 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. However, the new ATCOR 3 program
(Richter, 1998) seems to solve some of the problems addressed above.
2.2.5 Data Preparation (WP8)
Today’s remote sensors offer a wide variety of image data with different geometric, radiometric and
spectral resolution characteristics. 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-sensoral image data appropriate techniques for image fusion are
indispensable. Within ALPMON two methods were applied that allow the inclusion of features derived
from very high resolution panchromatic images into multi-spectral classification. The first method uses
object edges from the panchromatic image to sharpen the multi-spectral image, the second method
computes texture images to be used as additional bands in classification.
Image Fusion
Examining existing methods for fusion of panchromatic and multi-spectral images, the most common
approaches are pixel based techniques. These techniques apply numerical combinations of pixel
values (spectral reflectance values), and are often referred to as image merging (Chavez et al. 1991).
They include Intensity-Hue-Saturation and Principal Component substitution merging, linear
combinations and multiplication of image bands. A comprehensive review on these techniques can be
found in Pohl (1996). All these methods enhance the local texture of the multi-spectral image, thus
improving the visual interpretability of the fusion results. At the same time they distort the spectral
properties of the multi-spectral bands by combining spectral reflectance values of different image
bands.
In order to avoid the spectral distortion it is necessary to perform image fusion on a higher level of
abstraction. Instead of using the spectral reflectance values represented in the single pixels, the fusion
process can be based on image features, such as edges or segments. Within the project a new
approach was developed, applying adaptive filters for feature based image fusion.
The conclusions drawn from the application in the test sites are manifold. The adaptive image fusion
(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 multispectral
pixels, which is due to missing information in the panchromatic image. However, for the
segmentation of forest and non forest areas the approach is considered valuable. One suggestion was
to use the fusion result for first level classification, i.e. classification of meta classes that can be used
as masks for detailed classification. The advantage of this approach lies in the sharp delineation of the
objects to be classified, thus leading to less misclassifications caused by mixed pixels. The approach
has been used successfully for delineation of the forest border in the Dachstein test site.
A second suggestion was the combination of AIF and substitution techniques for visualisation. AIF
would then sharpen object edges and thus eliminate the blocky pixel structure of the low resolution
image. Next the local texture could be added by applying a substitution technique onto the AIF
processed image, thus leading to a sharpened high resolution multi-spectral image product. However,
this product is not considered an appropriate input for numerical classification but offers an excellent
visual impression of the area. The latter approach was successfully applied to the Tarvisio test site.
Texture Features
Very high spatial resolution panchromatic images contain a large amount of objects. In order to
recognise these objects the human visual system does not only rely on the intensity of single pixels but
on the spatial variation of these intensities, in general called texture. In contrast to the parameter tone,
which is defined by the intensity of a single pixel, texture has a higher degree of dimension. This
degree is not limited by the two dimensions of the image matrix but also includes the different
characteristics of texture. Using human language texture might be described as rough, smooth,
granular or by similar expressions. But these lingual descriptions lack a formal definition of texture.
Therefore most methods for its characterisation are based on the intuition and perception of the
JR, RSDE, ALU, LMU, Seibersdorf, WSL 21