Full text: Fusion of sensor data, knowledge sources and algorithms for extraction and classification of topographic objects

International Archives of Photogrammetry and Remote Sensing, Vol. 32, Part 7-4-3 W6, Valladolid, Spain, 3-4 June, 1999 
Fig 2. Workflow within a common classification process. 
A main aim of our project is to create a training set database 
once (for the first scene or date). In fact, we use the outlines 
of the training sets and apply them to other scenes. The main 
advantage is, that we don’t run into the pitfall that the same 
class has variations in spectral behaviour because of temporal 
and solar differences - this would definitely occur, if we just 
transfer collected signatures which belong to the first scene. 
Ideally, training data should be collected from multiple 
sources. Our training sets consist mostly of ATKIS data, 
Topo-Maps and ground truth data. This data should be used, 
on one hand for extracting training areas, and on the other for 
evaluation of the results. In principle, we could introduce 
errors, if we use training set boundaries of different sources 
in different scales (spatial/time). To avoid this, we use the 
one database with the best accuracy, which fits spatially and 
temporally to the image acquisition. If this can’t be carried 
out, there are three possibilities to handle this problem: 
Fig. 4. Process of signature extraction. 
(1) we could use the intersection of both databases by 
applying overlay techniques, (2) we could use the union by 
utilising join processes, and (3) we could use both databases 
separately. We avoid the 3 rd method because it causes a 
statistical overweighting in the classification process. After 
each GIS process flow, we perform a histogram check for 
each new signature. If we get a valid signature, we keep the 
file, otherwise it will be excluded from our database (Fig. 4). 
Within this workflow, our strategy is not to detect areas 
where changes have occurred but those without changes. If 
changes have occurred during the process of transferring 
training set boundaries to a new scene, we have to modify the 
geometry of the signature boundaries by using GIS 
capabilities (Fig. 4), such as buffering (dilate, shrink), until 
the signature becomes unimodal. We do not make use of 
thresholds between peaks of multimodal histograms to 
separate the different signatures, because this technique is not 
practical for automation. In the worst case, multimodal 
signatures have to be eliminated. A problem is to treat 
classes, which are unimodal in the first and second time stage 
Fig. 3. Overview of a modem workflow within an integrated GIS and Remote Sensing environment.

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