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Title
Fusion of sensor data, knowledge sources and algorithms for extraction and classification of topographic objects
Author
Baltsavias, Emmanuel P.

International Archives of Photogrammetry and Remote Sensing, Vol. 32, Part 7-4-3 W6, Valladolid, Spain, 3-4 June, 1999
meadow meadow to farmland
farmland Ä; farmland to meadow
Fig. 5. Change detection analysis from 1991 to 1995.
but differ thematically, e.g., if we use a unimodal forest
training boundary with a new scene and we find at the same
area meadowland also with a unimodal behaviour. To
exclude such errors, after a change detection analysis we
detect the training areas with changes and eliminate them
from the database. Of particular importance is a large size for
each training area. If an automated inside buffering reduces a
training area to a size that is no longer useable, this area has
to be eliminated. The size of the training set for each class in
the classification should be at least 30 times the number of
discriminating variables (e.g., bands) in the analysis (Swain
and Davis, 1978).
The next step is a classification strategy adjusted especially
for monitoring analysis. We use common robust
classification algorithms (e.g. Maximum Likelihood) in a
knowledge-based hierarchical process to discriminate
between the major landcover types (Rhein and Ehlers, 1996).
After the first classification pass (after the first date), the
results can be used to generate new training sets to be stored
in our information database.
Improvement of accuracy is the ultimate goal, which we try
to reach through our methods. To measure the accuracy, we
not only use standard accuracy assessment methods (e.g.,
contingency matrices). Use is also made of per-pixel values
of the Mahalanobis distance corresponding to the highest
membership. This technique is mentioned also at D’ Urso
and Menenti, 1996. Overlay and mask techniques, as well as
GIS database queries, make it possible to eliminate those
pixels whose confidence level is lower than a predefined one.
5.2. First Results
Using the above techniques, a first test of our method was
carried out to analyse changes in agriculture areas. Figure 5
shows the change detection analysis within a farmland area.
Changes have occurred from meadowland to farmland and
vice versa from the year 1991 to 1995.
6. CONCLUSIONS
First steps of an automated approach for training data
selection within an integrated GIS and remote sensing
environment for monitoring temporal changes are presented.
The future activities will focus on the integration and
automation of the classification strategies. An extension to
multimodal classes, like urban areas, will be aimed at.
Beyond this, a more formal statistical and quantitative
approach concerning training classes has to be addressed.
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