ALPMON FINAL REPORT - ARC systems research

Contract ENV4-CT96-0359 ALPMON
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.
A second application is the combination of AIF and substitution techniques for visualisation. AIF then
sharpens object edges and thus eliminates the blocky pixel structure of the low resolution image. Next
the local texture is 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.
3.3.6.2 Texture analysis
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
individual investigators. However, quantification of texture in digital images has been in the focus of
research for the last two decades.
Within the ALPMON project second order statistics using grey-level co-occurrence (GLC) matrices
were used to describe texture in images. A set of textural features can be derived from the GLC
matrices, that can be assigned as grey values to the pixels of an image. These textural feature images
have been successfully applied in order to improve automatic classification of remotely sensed
imagery.
Implementation of the texture analysis algorithms and their application to data sets from the test sites
show the benefits and limitations of the method. Some of the limitations are caused by quality of the
data sets. Image noise will always affect texture analysis, as it produces local variation of grey values
that are not related to the actual image content. This effect is particularly strong when using texture
features that do not take into account grey value differences. The second parameter that has an
influence on the texture analysis is the actual grey value range of the image. The smaller it is the less
transitions between different grey values are possible, thus reducing the range of textural information
available. Furthermore the grey value range is linked with the effect of noise. Assuming a Gaussian
distributed noise with a certain standard deviation, its effect will be stronger on a small grey value
range than on a larger one, as the ratio between signal and noise will decrease with an increasing grey
value range.
When using texture algorithms one has to keep in mind, that texture cannot be derived from a single
pixel but is always related to an area. Therefore the single pixel of a texture feature image actually
represents not only the texture of itself but also of its neighbours. This leads to a spatial generalisation
of the texture feature image although its nominal resolution might be the same as the one of the
original image. The degree of generalisation depends on the parameters used for calculating the
texture features. The larger the window size the stronger will be the effect of spatial generalisation. On
the other hand a small window will only analyse a small number of transitions. As the texture features
are statistical measures it is questionable if the sample derived from such a window is sufficient for an
estimate.
Looking at the results of the implementation in the test sites one can get an impression of the kind of
objects that can be detected by texture analysis. Settlements with their high variation of objects, linear
structures such as roads or railways are clearly visible in texture feature image. In addition transition
areas between two land cover types with significant different reflection characteristics will appear as
highly textured areas. The use of texture analysis for the discrimination of different forest stands
seems not to be possible. Taking into account the spatial resolution of the images (5-10m), the
comparably low grey value range (