Decision fusion is one of hot research topics in classification area, which aims to achieve the best possible performance for the task at hand. In this paper, we investigate the usefulness of this concept to improve change detection accuracy in remote sensing. Thereby, outputs of two fuzzy change detectors based respectively on simultaneous and comparative analysis of multitemporal data are fused by using fuzzy integral operators. This method fuses the objective evidences produced by the change detectors with respect to fuzzy measures that express the difference of performance between them. The proposed fusion framework is evaluated in comparison with some ordinary fuzzy aggregation operators. Experiments carried out on two SPOT images showed that the fuzzy integral was the best performing. It improves the change detection accuracy while attempting to equalize the accuracy rate in both change and no change classes.
Change Detector Combination in Remotely Sensed Images Using Fuzzy Integral
27.11.2007
oai:zenodo.org:1333480
International Journal of Electrical, Electronic and Communication Sciences 0.0(11)
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
DDC: | 629 |
Digital processing of remotely sensed images
TIBKAT | 1980
|Multispectral Data Compression of Remotely-Sensed Images
British Library Conference Proceedings | 1993
|Shape Recognition Schemes In Remotely Sensed Images
British Library Conference Proceedings | 1996
|