Change Detector Combination in Remotely Sensed Images Using Fuzzy Integral

Authors: H. Nemmour, Y. Chibani

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.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1333480

References:

[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.