ALPMON FINAL REPORT - ARC systems research

Contract ENV4-CT96-0359 ALPMON
area of interest within 7 days. Apart from our fundamental doubts, growing from our experiences with data
availability of operational systems of the last twenty years, Landsat, Spot but also IRS, with swath width
larger for the factor 6 to 17, this is only to be realised by changing the view angle from path to path. This
directly led to the necessity to adopt and improve the existing geocoding as well as topographic /
atmospheric correction software.
Data preparation
Image fusion techniques as applied in the project can improve significantly the quality and accuracy of
land cover classification. However, the type of land cover objects that actually benefit from fusion
techniques is still somewhat limited. In particular the loss of texture information for the price of spectral
preservation is a drawback. Recent research results have shown that image fusion based on wavelet
transform preserve both texture and spectral information.
Image analysis with special respect to the new sensor generation
With the acquisition of high-resolution data, which with the launching of IKONOS has only been made
available at the end of ALPMON, there will be an increased need of appropriate processing algorithms.
High-resolution data produces increased variance of objects, thus new classification methods need to
cope with this increased variance.
The problem of high pixel value variance characterising objects of interest in high resolution remote
sensing data prohibits the automation of evaluation chains for data with geometric resolutions in the „m“
scale (airborne scanner data, Ikonos, Quick Bird, etc.) with conventional commercial software systems.
Traditional image analysis software follows the feature space concept. Pixel by pixel the whole image is
sorted into classes, according to their DN value in the spectral domain, which defines their position in the
feature space. The results are X classes with Y number of pixels. Clusters of pixels of the same class are
interpreted to belong to one object. The location of the objects is unknown. The statistics are calculated
class-wise.
On image processing level object oriented classification approaches, based on spectral as well as on
textural and shape parameters and being able to use context as well as auxiliary information in
classification process wake expectations in more accurate classification results. Two new software
packages shall be mentioned in this context: the ClassTool currently developed at Joanneum
Research, and the Delphi2 eCognition. LMUs 20 month experiences with eCognition (Buck et al., 1999,
a,b, de Kok et al, 1998, 1999 a,b, Schneider et al., 1999, 2000) proves, that this software is able to solve
or at least to bypass some of the bottlenecks of conventional image processing:
� methodology for the automatic classification of high resolution satellite data
� methodology for data fusion, data merge, image fusion, data concatenation, sensor merge,
resolution merge
� the scaling problem.
Furthermore, the ClassTool software currently developed at Joanneum Research, is a very flexible tool
which is specialised for feature definition and extraction, shape extraction, segmentation, statistical
analyses, and object based classification with various classification algorithms.
The concept of object oriented systems is different in some key positions. Object oriented image analysis
systems starts with an image segmentation. Usually the segmented image is than stored as bitmap. The
innovative step forward made by programs like the tested Delphi2 eCognition is to store the statistics
calculated for creating a segment for each object as a attribute table. This, on the first view trivial solution,
opens the door to the GIS world. First, each segment is defined by its location and becomes an object
with properties stored as attribute table, and, second, the objects can now be selected by .sql queries of
relational data base systems. GIS layers, respectively the connected data bases can directly be integrated
into the search. This way the problem of data merge is practically solved. Additionally, the fuzzy logic
classification tools of eCognition approach the week “natural” transitions in alpine environment much
better than the Boolean logic of conventional image processing software packages.
The task driven definition of hierarchical evaluation rules becomes the key ability for image analysis.
Which are the advantages of an object oriented system for monitoring systems ?
Having the duty to administrate area covering objects like forests (forest management), lakes, river and
riverbeds (water management), agricultural plots, biotops (environmental survey), a.s.o., the most
effective way is to do it object-wise. Normally for each area, defined as an object of interest, a data base
has been established at the moment this object is assigned to an administration. Searching for
information about an area it is sufficient to know its Id No. or geographic location.
JR, RSDE, ALU, LMU, Seibersdorf, WSL 88