The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B4. Beijing 2008 
mapping but rather as a “notification/indication” of change with 
a possibility to indicate geometric and attributive change occur 
rences (i.e. class transition(s)). Therefore, the output of the De- 
COVER change-detection procedure is a GIS-data-layer 
(change-layer), which holds the geometry of detected changes 
(change-objects) plus information about the assumed changes 
together with indications for the plausibility of these assump 
tions. Thus, it relates to objects as part of an existing database 
but compares two images at different dates on a per-pixel level, 
giving a pixel- and segment-based indication of LU/LC change. 
Methods for the comparison of images from different dates may 
be grouped into those which use univariate image differencing 
alone (Singh, 1989, Fung, 1990), methods to compare vegeta 
tion properties like NDVI or Tasseled Cap Tranformations 
(Richards, 1993), or change vector analysis (Lambin, 1994, 
Bruzzone et al, 2002). A comprehensive overview of existing 
pixel based techniques, their advantages, disadvantages and 
resulting accuracies is given by Lu et al, 2004. Relating pixel- 
based indications to objects means developing an approach for 
integrating these indications into an object based analysis.There 
are several ways to implement such a procedure as has been 
shown by different authors (Schopfer, 2005, Busch et al., 2005 
and Gerke et al., 2004). Because users of DeCOVER prefer 
some type of a “change notification”, which might also be use 
ful for their specific application, like updating of user operated 
databases (i.e. ATKIS) by proprietary techniques an attempt as 
described in the following chapters has been made to set up and 
realize a prototypical framework for CD. The framework is split 
into two main modules: a focusing module and a classification 
module. Both have been designed and realized in close coopera 
tion between the company GeoData Solutions (GDS) and IPI. 
2.1 Focusing Module 
Within the CD-concept this module plays an important role, 
since it outlines potential changes by generating change- 
segments based upon per-pixel change indicators. Steps of pre 
processing, necessary to render the two images comparable in 
both the spatial and spectral domains are included (co 
registration, radiometric normalization). Figure 2 shows an 
outline of the focusing module. 
Figure 2: The Focusing Module. 
With respect to the spatial domain miss-indications by displaced 
pixels in both images should be avoided. However, due to dif 
ferences in illumination and view angles some “change noise” 
is still unavoidable. In order to outline changes indicated by a 
per-pixel comparison of the images within the focusing module, 
an image segmentation based upon one or more pixel-based 
change-indicators is applied. This way, not the changes them 
selves, but borders between different change indication-values 
are generated, which consequently leads to a spatial differentia 
tion of change- (high indicator values) and no-change-areas 
(low indicator values) in terms of changes in signal. These seg 
ments can be subsequently handled as image objects (of change) 
which consequently offers the whole palette of object based 
image analysis (see Blaschke, T. et al, 2008; Schöpfer, E., 
2005). Although all of these advantages of segmenting indicated 
changes, the typical drawbacks of this approach cannot be de 
nied: the generated image objects should ideally represent true 
change-objects, which means: each change should be repre 
sented by only one image-object (no over- or under 
segmentation). This in turn leads to the problem of finding suit 
able segmentation algorithms and parameterizations. 
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Figure 3: Illustration of determining plausible changes (Pt’) for 
a segmented change-object by combining fuzzy-assignments to 
indication-classes (indication A to C) using different statistical 
parameters on a per-object basis for each indicator with a-priori 
probabilities of change (Pt). The segmentation is based upon 
one or more pixel-based change-indicators (II to In). 
Last but not least, comparing for each change-object its class 
assignment in the mapping of the GIS-database (DeCOVER tO), 
the a-priori probabilities (Pt) of change for this class and the 
statistics of the underlying (indicator-) images leads for each 
segment to an estimation, whether the segment outlines a 
change and if so whether the type of change, i.e. the new class 
can be determined automatically or manually. Vice-versa, each 
object of the DeCOVER-tO-mapping can be marked as changed 
as soon as it covers or overlaps a change-object. 
2.2 Segmentation and Classification Module 
Within the classification module in principle the last step of the 
focusing module is recursively applied for each change-object 
until the most plausible change, i.e. the most plausible class at 
the second point of time (tl) can be determined. In the worst 
case no such evidence can be given except that a change in sig 
nal has been detected, but its class-assignment in tl remains 
unclear. In all other cases each change-object can be assigned to 
one or more classes with a certain degree of a-priori probability