Change Detector Combination in Remotely Sensed Images Using Fuzzy Integral
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.
[1] Bárdossy, A., and Samaniego, L. (2002). Fuzzy Rule-Based
Classification of Remotely Sensed Imagery. IEEE
Transactions on Geoscience and Remote Sensing, 40, 362-
374.
[2] Cho, S. B. (1995). Fuzzy Aggregation of Modular Neural
Networks With Ordered Weighted Averaging Operators.
International journal of approximate reasoning ,13, 359-375.
[3] Cho, S. B., and Kim, J. H. (1995). Combining Multiple
Neural Networks by Fuzzy Integrals for Robust
Classification. IEEE Transactions on Systems, Man and
Cybernetics, 25, 380- 384.
[4] Cho, S. B. (2002). Fusion of Neural Networks with Fuzzy
Logic and Genetic Algorithm. Integrated Computer-Aided
Engineering, IOS Press, 9, 363-372.
[5] Deer, P. (1998). Digital Change Detection in Remotely
Sensed Imagery Using Fuzzy Set Theory. PHD thesis,
Department of Geography and Department of Computer
Science, University of Adelaide, Australia.
[6] Foody, G.M., McCulloch, B., and Yates, W. B. (1995).
Classification of Remotely Sensed Data by an Artificial
Neural Network: Issues Related to Training Data
Characteristics. Photogrammetric Engineering & Remote
Sensing, 61, 391-401.
[7] Kittler, J., Hatef, M., Robert, P. W., and Matas, J. (1998). On
Combining Classifiers. IEEE Transactions on Pattern
Analysis and Machine Intelligence, 20, 226-239.
[8] Bárdossy, A., and Samaniego, L. (2002). Fuzzy Rule-Based
Classification of Remotely Sensed Imagery. IEEE
Transactions on Geoscience and Remote Sensing, 40, 362-
374.
[9] Lambin, E. F., and Strahler, A. H. (1994). Change vector
analysis in multitemporal space : a tool to detect and
categorize land cover change processes using heigh temporal
resolution satellite data. Remote Sensing. Environ, 48, 231-
244.
[10] Lipnickas, A., Malmqvist, K., and Verikas, A. (2000). Fuzzy
Measures in Neural Networks Fusion. ISSN 1392-124X
Informacins Technologijos, 2, 7-16.
[11] Lipnickas, A. (2001). Classifiers Fusion with Data
Dependent Aggregation Schemes. In proceedings of
International Conference on Information Networks, Systems
and Technologies, 147-153.
[12] Liu, Q. Z. et al. (2001). Dynamic Image Sequence Analysis
Using Fuzzy Measures. IEEE transactions on systems, man,
and cybernetics-Part B: Cybernetics, 31, 557- 572.
[13] Verikas, A., Lipnikas, A., Malmqvist, K., Bacauskiene, M.,
and Gelzinis, A. (1999). Soft Combination of Neural
Classifiers: A Comparative Study. Pattern recognition letters,
Elsevier Science, 2, 429-443.
[14] Fullér, R. (1995). Neural Fuzzy System. , .Åbo Academy,
Sweden.
[1] Bárdossy, A., and Samaniego, L. (2002). Fuzzy Rule-Based
Classification of Remotely Sensed Imagery. IEEE
Transactions on Geoscience and Remote Sensing, 40, 362-
374.
[2] Cho, S. B. (1995). Fuzzy Aggregation of Modular Neural
Networks With Ordered Weighted Averaging Operators.
International journal of approximate reasoning ,13, 359-375.
[3] Cho, S. B., and Kim, J. H. (1995). Combining Multiple
Neural Networks by Fuzzy Integrals for Robust
Classification. IEEE Transactions on Systems, Man and
Cybernetics, 25, 380- 384.
[4] Cho, S. B. (2002). Fusion of Neural Networks with Fuzzy
Logic and Genetic Algorithm. Integrated Computer-Aided
Engineering, IOS Press, 9, 363-372.
[5] Deer, P. (1998). Digital Change Detection in Remotely
Sensed Imagery Using Fuzzy Set Theory. PHD thesis,
Department of Geography and Department of Computer
Science, University of Adelaide, Australia.
[6] Foody, G.M., McCulloch, B., and Yates, W. B. (1995).
Classification of Remotely Sensed Data by an Artificial
Neural Network: Issues Related to Training Data
Characteristics. Photogrammetric Engineering & Remote
Sensing, 61, 391-401.
[7] Kittler, J., Hatef, M., Robert, P. W., and Matas, J. (1998). On
Combining Classifiers. IEEE Transactions on Pattern
Analysis and Machine Intelligence, 20, 226-239.
[8] Bárdossy, A., and Samaniego, L. (2002). Fuzzy Rule-Based
Classification of Remotely Sensed Imagery. IEEE
Transactions on Geoscience and Remote Sensing, 40, 362-
374.
[9] Lambin, E. F., and Strahler, A. H. (1994). Change vector
analysis in multitemporal space : a tool to detect and
categorize land cover change processes using heigh temporal
resolution satellite data. Remote Sensing. Environ, 48, 231-
244.
[10] Lipnickas, A., Malmqvist, K., and Verikas, A. (2000). Fuzzy
Measures in Neural Networks Fusion. ISSN 1392-124X
Informacins Technologijos, 2, 7-16.
[11] Lipnickas, A. (2001). Classifiers Fusion with Data
Dependent Aggregation Schemes. In proceedings of
International Conference on Information Networks, Systems
and Technologies, 147-153.
[12] Liu, Q. Z. et al. (2001). Dynamic Image Sequence Analysis
Using Fuzzy Measures. IEEE transactions on systems, man,
and cybernetics-Part B: Cybernetics, 31, 557- 572.
[13] Verikas, A., Lipnikas, A., Malmqvist, K., Bacauskiene, M.,
and Gelzinis, A. (1999). Soft Combination of Neural
Classifiers: A Comparative Study. Pattern recognition letters,
Elsevier Science, 2, 429-443.
[14] Fullér, R. (1995). Neural Fuzzy System. , .Åbo Academy,
Sweden.
@article{"International Journal of Electrical, Electronic and Communication Sciences:56345", author = "H. Nemmour and Y. Chibani", title = "Change Detector Combination in Remotely Sensed Images Using Fuzzy Integral", abstract = "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.", keywords = "change detection, decision fusion, fuzzy
logic, remote sensing.", volume = "1", number = "11", pages = "1621-7", }