The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B4. Beijing 2008 
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and an indicated type of change (e.g. increase or decrease of 
vegetation). Thereby, the a-priori probability is taken from a so- 
called transition-probability-matrix (Pt-matrix), which will be 
explained in chapter 3.1 in more detail, whereas the indicated 
type of change is given by the fiizzy-combination and -analysis 
of indicators (object-based statistics) and derived indicator- 
classes (see chapter 3.3). Thus, the combined analysis of a- 
priori probabilities of transition (Pt) together with fuzzy- 
membership-degrees (p) to indicator-classes can be seen as a 
plausibility check of change, whereas the most plausible change 
(Pt’) can be understood as the most likely change. Referring to 
the envisaged content of the change-layer, for each change- 
object its most plausible change (Pt’), together with its indica 
tions (membership-degrees to indication-classes) can be deter 
mined (see Figure 3). Thus, after applying some generalization 
rules to the outlined change-objects the output of the classifica 
tion module is exactly the desired change-layer. Regarding the 
three-level-hierarchy of the DeCOVER-nomenclature (see De- 
COVER, 2008) it is obvious that some changes can only be 
assigned in the first or second class-level automatically, while 
others are even assignable in the third level. Therefore, as soon 
as for a detected change no clear assignment can be given, it has 
to be determined manually or left as not assignable. 
3. IMPLEMENTING THE CONCEPT 
As described in chapter 2, besides the delineation of changes, 
each object of the change layer shall be given one or more 
likely classes for tl and some tangible terms of expressing the 
plausibility for each assumed tl-class. A very central point in 
determining the plausibility of a detected change is the consid 
eration of the a-priori probability of a class to exchange from 
the current (tO) class assignment to another in tl. 
3.1 Setting-up the Pt-Matrix 
Assuming that the DeCOVER class-hierarchy and its nomencla 
ture applies at the point of time tl the same way, as it did in tO 
and assuming that a change indicated by the comparison of the 
image data of tO and tl indicates a change of class assignment, 
then in principle such an indicated change simultaneously indi 
cates a change from the class tO to another out of the 38 possible 
classes at tl at the indicated position. Since we know a-priori 
that some transitions or changes of an object are very unlikely 
or even impossible within a given time, e.g. such as from dense 
urban area to glacier within a period of two years, while others 
are relatively likely, e.g. from arable land to sparse urban area 
or construction site within the same period, we can assume that 
for an indicated change if there was arable land before the 
probability of being sparse urban area or construction site at 
the indicated position now is higher than being a glacier there. 
I.e.: we can skip procedures which tend to verify unlikely tran 
sitions. The probability of transitions from one class to others 
can be recorded in an n x n matrix whereas n is the number of 
classes. However, for some transitions reliable values are hard 
to determine without expert knowledge or analyzing historic 
mappings and statistics in detail. Thus, we decided to fill the Pt- 
matrix in collaboration with the experts of the DeCOVER con 
sortium, whereas each Pt-value is normalized to the range of 
[0.0; 1.0]. By sorting each Pt-vector according to the Pt-values 
one obtains the most and least probable classes. Nevertheless, 
each Pt-value has to be seen as a heuristic value and does not 
claim to be 100% true. 
3.2. Creating change-objects by segmenting per-pixel indica 
tors 
In order to keep the results based on the IKONOS- and SPOT5- 
data comparable, for all further investigations the IKONOS-1 
(blue) and SPOT5-4 (swir) channels were skipped. As reported 
in Lohmann, P. et al., 2008 several pixel-based change indica 
tors have been investigated regarding their suitability for a fo 
cusing module as described here. Therefore a comparison be 
tween the indicators and a manual change classification regard 
ing the categories Urban and Vegetation has been undertaken. 
The results were relatively poor due to several reasons. One of 
them is the “change-noise” which is mostly caused by different 
illumination situations. However, it turned out in this investiga 
tion, that the principal components (PC) generated out of the 
channels of tO and tl and the (normalized) difference of corre 
sponding channels (Diffnorm) show the best results. As the 
authors note, one of the reasons for the relative poor results lies 
in the reference used, which was a manual digitizing of recog 
nizable changes. Respectively, objects were compared to pixels. 
This means changes and “change-noise” was compared to a 
noise-free reference, which leads to an overestimation of false- 
positives and false-negatives on a per-pixel-comparison. In 
order to suppress “change-noise” and to obtain contiguous areas 
of change and no change respectively, an image segmentation 
based upon the per-pixel change indicators appears to be rea 
sonable for the following reasons: 
• The border of each change-object is generated along 
steep changes of change-indication depending on the 
thresholds of the used algorithms. 
• Thus, the generated objects can be regarded as more 
or less homogeneous areas of change or no change given 
by the indicators used for delineation. 
• Shape, texture and pixel statistics of each object can 
be used to analyze and classify it at least as change- or no 
change-object. 
Since the so called multi-resolution segmentation (MRS) de 
scribed by Baatz & Schàpe, 2000 uses criteria of homogeneity 
in color (here in change-indication) and shape it seams to be 
adequate to generate reasonable change-objects based on the 
per-pixel indicators. This means the criterion of color- 
homogeneity of the MRS is generated by the per-pixel-indicator 
values, which finally leads to objects of homogeneous change- 
indication. As reported by several authors (Meinel, G., et al, 
2001a; Meinel, G., et al, 2001b; Neubert & Meinel 2002a; Neu- 
bert & Meinel 2002b), a critical point of the MRS is its parame 
terization, i.e. to find a well balancing between over- and under 
segmentation of the desired objects. To overcome this drawback, 
an over-segmentation - i.e. too small neighboring objects with 
similar indication values - can be merged according to their 
mutual difference of indication values if the difference is below 
a defined threshold. Thus, the resulting change-objects are gen 
erated in a first step according to their homogeneity of indica 
tion values and shape and in a second step according to their 
similarity of indication values. Because of the results in Loh 
mann, P. et al., 2008 as a first mind it seams to be convincing to 
use the difference channels based upon the (normalized) green-, 
red- and nir-channel (Diff norm) and the principal components 
of the tO and tl channels (PC) as input for the segmentation. 
However, regarding the information content in terms of change 
or no-change both indicators are relatively redundant - espe 
cially PC2 und PC4 are highly correlated to the differences of 
the three spectral channels (see Table 2). Additionally, the in 
terpretation of PCs - especially of temporal PCs is relatively 
ambiguous so that the information content of each single PC is 
quite unclear. In the data present, regarding table 2 and table 3 
in conjunction, it is quite unclear, whether PC4 reflects the dif 
ference in the red and green channels or just the