The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B4. Beijing 2008 
311 
Image Object Information 
Evaluation of Class: VL -> BS, Bl, BV, FA. FN 
and (min) 
not homogeneous by area and SO 
Classification value of change by norm_Zt 
Classification value of decrease in vegetation >0.5 
increase of StdDevs 
Feature Value Weight 
not applicable 
not applicable 
not applicable 
not applicable 
Value 
0.457 
0.457 
0.457 
1.000 
1.000 
1.000 
44 Vi ►: ►» \ Features / Classification / Class Evaluation f~ 
Image Object Information 
Term 
Feature Value 
Weight 
Value 
Evaluation of Class: VL -> BS. BI. BV. FA. FN 
0.000 
and (min) 
0.000 
no! homogeneous by area and SO 
not applicable 
0.018 
Classification value of change by norm Zt 
0 784306338 
not applicable 
0.784 
Classification value of decrease in vegetation >0.5 
1 
not applicable 
1.000 
increase of StdDevs 
not applicable 
0.000 
< 
> 
4L 4 | ► »;\ Features / Classification /Class Evaluation / 
Figure 8: Evaluation of change-indication for objects to be 
marked as change (top) (here from agriculture to not vegetated) 
and no change (bottom). 
Identity Nom 
'1 - Jon Pt n 
1 VU 
<Top-n»Mtiâvër>" jE]f 
Location' 
3.373.155,303 ! - 
Field 
VST H 
FID 
244? 
5twc 
Polygon 
Osssf** 
0,457103 
l 
Oks/i_2 
1 
3 
I 
OKSfi.4 
0,542897 
Oâisfied 
1 
FID i 
2447 
mMCisjxic 
Via 
Pt I DeCOVE 
0,176056 
K2„DeCOVE 
0,175056 
«.3J>COVE 
0,060028 
PM.DeCOVE 
0,070473 
Pts DeCOVE 
0,070423 
Ft6_D«COVE 
0,070123 
Pt7J>«C0V£ 
0,052817 
Pt8J>eCOVE 
0,052817 
0,035211 
TÔC151 £*•:.- 
VLg 
TOaS2_OeC 
Vis 
TOC153 Dec 
VLk 
TOCLMjDeC BSg 
Toass 
-B5S 
TOCLS6 D»C 
61! 
TOaS7JDeC 
BSm 
Toasej*: 
BVs 
Toasojxc 
8Ss 
Ptljiew 
0,15594 
Pt2_new 
0,15594 
P43_i»w 
0,07797 
PMJ19W 
0,06 
Figure 9: Change-objects with class-assignments from De- 
COVER_tO (dark red labels) and indicated most probable class- 
assignments for tl (black labels) with a-priori Pt values (black 
lables) and Pt-values adjusted by //-values of indication classes 
(purple labels). Right: object table, whereas BSg is marked as 
the most probable class in tl 
This way, for each object its change indication and possible 
transition can be evaluated (see Figure 8 and 9).As Figure 8 
shows, an object is only marked as changed if for none of the 
indication classes a degree of membership of p - 0.0 is given. 
Additionally, it demonstrates the reliability of an indicated 
change by the overall membership degree given through the 
fuzzy-and operator. In the examples given, the top result indi 
cates a clear change, since p to almost each of the indicator- 
classes is 1.0. A reasonable way to combine the a-priori Pt- 
values with the degrees of membership in order to obtain one 
value to indicate the plausibility of an indicated change is to 
calculate the mean of the product of the Pt-value of an indicated 
transition and the //-values to the indicator-classes. Figure 9 
outlines the conjunction of a-priori Pt-values with membership- 
values to describe the transition of an object from agriculture 
(VL) to non-vegetated (BS, BI, BV, FA, FN). The marked 
object in Figure 9 is the same as in Figure 8 at top. Although the 
highest a-priori transition probabilities are given to VLg, VLs 
and VLk {grassland, other permanent crop and complex agri 
cultural) the indication-classes indicate a transition to non- 
vegetated. Thus, not the agricultural classes are given as the 
most probable (most plausible) classes but the most probable 
class out of the non-vegetation category, in the example BSg 
{low density urban area). 
3. CONCLUSIONS AND OUTLOOK 
The paper demonstrates the current stage of the development of 
automated procedures to outline potential changes and to evalu 
ate them automatically as far as possible within the context of 
the DeCOVER project. Methods were outlined which are capa 
ble to detect and outline changes in multi-temporal image data 
on the basis of per-pixel and per-object change indications. 
These outlined changes are analyzed by means of methods of 
object based image analysis and fuzzy class assignments. It has 
been demonstrated, how the fuzzy-membership of a detected 
change to defined indication classes in conjunction with a-priori 
knowledge about transition probabilities can be combined in 
order to give evidence about a change in terms of the De 
COVER nomenclature. The current stage of these developments 
has to be seen as still prototypical. Expected aspects that emerge 
to become objects of further research in this field are: improv 
ing the reliability of a-priori transition probabilities by taking 
sound analyses of historic information and spatial context into 
account. Especially within an operative environment it seems to 
be reasonable to adapt the current probabilities in accordance 
with changes already detected (“self-learning Pt-matrix”). Spa 
tial context has not been considered yet, although its influence 
on the probability of a transition cannot be denied. However, in 
order to focus on potential change-areas and to give indications 
about what could have happened, the current status seams to be 
capable to reduce manual effort. Within the context of change 
segmentation on the basis of indicators, there is still demand 
about optimizing algorithms and their parameterization.