International Archives of Photogrammetry and Remote Sensing, Vol. 32, Part 7-4-3 W6, Valladolid, Spain, 3-4 June, 1999 
In 
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Above applications are in 2D and increasingly in 3D, while 
multitemporal (4D) approaches are still rare. 
Quite long is the list of problems, which are encountered with 
respect to data and information fusion: 
• Differences between landuse (provided by GIS) and landcover 
(provided in images). 
• Lacking procedures for interpretation and quality control of 
fused images. 
• Fusion and the mixed pixels problem. 
• Distortion of spectral properties with pixel-based fusion 
techniques (partially avoidable by feature-based fusion). 
• Different levels of data quality regarding geometric accuracy 
and thematic detail. 
• Large differences in fusion of multitemporal data and change 
detection. 
• Differences between the data in spatial and spectral resolution 
(centre and width of band), as well as polarisation and angular 
view. 
• Data generalisation in map and GIS data. 
• Different levels of data abstraction and representation 
(resolution/scale). 
• Models of objects used in image analysis are often simplistic, 
limited in number and not general enough; on the other hand, 
generic models may be too weak and broad. 
• Different data representations, even for the same object or 
object class (roads, road networks). 
• Different data structures for the same object (e.g. raster, 
vector, attribute). 
• Lack of accuracy indicators for the components to be fused. 
• Data are often inhomogeneous, i.e. acquired by different 
methods, several analysts etc. 
• Algorithms to fuse information are restrictive, mechanical, not 
intelligent enough. 
• Abrupt decisions (as humans take sometimes) are not 
permitted by algorithms. Rules/models (e.g. roof ridges are 
horizontal) always have exceptions, but still should be used, 
e.g. with associated probabilities which can be updated by 
accumulation of knowledge, processed data etc. 
• Rules/models differ spatially (e.g. buildings in Europe differ 
from those in developing countries) and in time (old buildings 
differ from new ones). 
• Architecture of systems is complex, processing requirements 
high, commercial systems or support tools are limited or non 
existent. 
• Gap between research and practice (which is typical for not 
matured scientific areas). 3 
3. USE OF GIS DATA AND MODELS IN IMAGE 
ANALYSIS 
GIS data and generally object models are generally used to 
provide information (geometrical, spectral, textural, functional, 
temporal etc.) about the target object(s), its attributes, as well as 
other objects and information related to the target ones. GIS 
information can be used in image analysis for various purposes: 
• Provision of initial approximations for some unknown 
parameters and thus reduction of the search space and 
increase of the probability of success. 
• Provision of clues for target objects based on information on 
other objects, related to the target ones, e.g. use of existing 
information on road network to detect buildings. 
• Quality control of the results, e.g. by serving as "ground 
truth". 
• In classification, e.g. in supervised classification to 
automatically select training areas, and in unsupervised one to 
automatically assign detected information classes to thematic 
classes. 
• Use of provided cues and information in various stages of 
object recongnition and reconstruction, e.g. to: (a) find 
objects, e.g. buildings by extracting the 3D blobs of a given 
DSM ; (b) exclude wrong hypotheses and detect blunders, (c) 
exclude regions that are impossible for a target object, e.g. 
building or road in water surfaces. 
• Use of data to support hypothesis generation about the model, 
e.g. try to infer the roof type (or some possible ones) based on 
the given building outline. 
In most cases, the integration of images analysis and GIS can 
not lead to full automation. Thereby, human interaction and 
intervention becomes necessary. The important points are how 
and when this interaction should occur. Generally, the 
interaction can (a) be preventive or have a guidance character, 
and (b) be corrective. Usually, the first case occurs at the 
beginning of the processing, the second one at the end. Whether 
the first or second approach is more appropriate depends on 
how much the quality and efficiency (time aspects) of the whole 
process are improved, as the result of such interaction. In this 
respect, preventive approaches seem to be preferable. Human 
intervention is also necessary to define the framework of the 
solution to a given problem: 
• Analysis of the problem, definition of the strategy. 
• Selection of building blocks that should be used (data, 
knowledge sources, processing methods etc.). 
• Decision on interactions between the blocks and definition of 
the processing flow. 
4. PREREQUISITES FOR INFORMATION FUSION 
Before fusing and integrating information, several prerequisites 
should be fulfilled: 
Co-registration. By this we mean that the different components 
should be compatible/comparable with respect to various 
aspects: spatial (data should refer to the same area and 
coordinate system), temporal, spectral (inch appropriate 
corrections due to terrain relief, atmosphere, sensor calibration), 
resolution (pixel footprint, number of bits per pixel). Spatial co 
registration is always a prerequisite. An overview of co 
registration and geocoding methods is given in Raggam et al., 
1999 (these methods should be appended by the increasingly 
used direct sensor data geocoding methods, using integrated 
GPS and INS). Depending on the application and the level of 
fusion, co-registration with respect to some of the above aspects 
is not necessary. E.g. while image fusion of multispectral and 
high resolution SAR imagery might be incompatible and not 
appropriate, integration of object cues from such images might 
be feasible and desirable. The same applies to temporal co 
registration, e.g. while for object extraction multiple data of the 
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