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4. CONCLUSION
In this paper, the applicability of change detector combination
was investigated. Our assumption was that the change detection
accuracy in remotely sensed data can be increased by
combining different change detectors. Thereby, two fuzzy
change detectors based respectively on the comparative analysis
and the simultaneous analysis of multitemporal data were
combined by using the fuzzy integral. Both change detectors
used a fuzzy membership model computed by taking the
squared Mahalanobis distance from the prototypes of the
classes. Experiments using SPOT hrv data of the same area
demonstrate that the combined change detection system with the
fuzzy integral outperforms the individual change detectors. It
increases the detection rate while reducing the number of false
alarms. However, even though the usefulness of combining
change detectors was highlighted, it has been shown that in a
given land cover class, if one of the individual change detectors
gives a very poor accuracy and the second gives an important
accuracy, the precision of the combination system will be
smaller than that of the most precise system (This is the case of
the class 7). We think that this problem may be avoided when
combining more than two change detectors.
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International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B3. Istanbul 2004
