Roeland de Kok 
  
of which terrain knowledge is available, is correctly classified as a coniferous-middle stand. The critical band here is 
mean value of band 4, SPOT —4 , infra red, giving a 92.3% class membership. The second class option, the object 
belongs 38.6 % to the class coniferous-open (figure 5) . Also in this case, mean value of Band 4 from SPOT-4 is the 
critical feature. Gaussian distribution can be assumed, explaining the shape of the fuzzy logic curves in figure 3. For 
other features, such as standard deviation of the panchromatic band, the Gaussian assumption is not quite correct. In this 
case a box classifier is better at place (see figure 2). Gaussian distribution can be checked in a 2-D plot of independent 
object features (Figure 6). An explanation for the shape of the graph in figure 7; Gaussian distribution for panchromatic 
data is corresponding to expectation for object mean value and standard deviation (range X=15-210).Mean value 
depends on photo count, standard deviation is a measurement of surface roughness. There is no factor that allows to 
assume that brighter objects are more smooth or vise versa. In very dark objects (shadow !), however variance is 
minimal. In very bright objects, the sensor saturates, therefore the variance turns to zero as well (Manakos, oral 
remarks). This curve is therefore a better indication for sensor sensitivity. The terrain knowledge allows a proper 
selection of typical representative objects and analysis of their critical features. After this phase, an overall evaluation 
can take place. For more automatic procedures, the difference between first and second membership function is an 
interesting one (Baatz, 1999). In the case of the object in figure 4 and 5 this would mean 92,28-38.6=53.68. If this 
object would be 99% member of coniferous--middle and 9796 member of coniferous- open the difference is only 2, Still 
the class membership is very high. This illustrates the difficulties of object evaluation and the deviation from class 
evaluation Until communis opinio on automatic procedures are not fully developed, visual check remains the basic 
evaluation procedure, but automatic alternatives are available and waiting for acceptance among the user community. 
6 CONCLUDING REMARKS 
The analysis on a per-pixel basis is not very useful in image data with high internal variance. To overcome this 
problem, an object based analysis allows a good solution. Object oriented analysis alone is not enough. In software like 
eCognition, a few bottlenecks are solved at the same time. First, a single database allows a full integration of GIS and 
remote sensing data. Using a hierarchical semantic network and a fuzzy logic ‘query’ facility on the database, the expert 
knowledge can be incorporated and the implicit information richness in the database can be fully exploited. Image and 
data fusion are a by-product from the visualization of the central database. The landscape model as defined in object 
layer construction and flexible database query through adjustable decision curves, requires much more terrain 
knowledge. This offers a extended role for the field expert. The software tools are very flexible and strong but the 
landscape model is very much depending upon user specifications. The landscape object model therefore is the crucial 
part of the analysis. For a specific application, exchange of modeling and object-sensor relationships becomes necessary 
between users. Spatial relationships in a hierarchical network is a rather new way of defining semantics between image 
and 'geo'- objects. Still this field is in full development and the results are promising enough to keep a keen eye on the 
developments. As literature showed, over the past decades there is a development in favor of object oriented analysis of 
environmental imagery and GIS data. Therefore these strategies are not a fashion flaw that comes and goes. This type of 
analysis is very well rooted in long term development and will dominate remote sensing research in the near future. 
Automatic accuracy assessment remains a very important topic. Confusion matrices are a too simple tool to achieve 
proper inside in the robustness of the classification. Only expert consensus can achieve the formulation of procedures 
that allow an acceptable level of automatic accuracy analysis. Object oriented analysis is here to stay; Educational 
remote sensing literature should add a chapter accordingly. 
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