A multiobjective fuzzy clustering method for change detection in SAR images

On account of the presence of speckle noise, the trade-off between removing noise and preserving detail is crucial for the change detection task in Synthetic Aperture Radar (SAR) images. In this paper, we put forward a multiobjective fuzzy clustering method for change detection in SAR images. The change detection problem is modeled as a multiobjective optimization problem, and two conflicting objective functions are constructed from the perspective of preserving detail and removing noise, respectively. We optimize the two constructed objective functions simultaneously by using a multiobjective fuzzy clustering method, which updates the membership values according to the weights of the two objectives to find the optimal trade-off. The proposed method obtains a set of solutions with different trade-off relationships between the two objectives, and users can choose one or more appropriate solutions according to requirements for diverse problems. Experiments conducted on real SAR images demonstrate the superiority of the proposed method.

In this paper, we put forward a multiobjective fuzzy clustering method for change detection in SAR images. As shown in the figure, the change detection problem is modeled as a multiobjective optimization problem, and two conflicting objective functions are constructed from the perspective of preserving detail and removing noise, respectively. A number of solutions representing different trade-off relationships between preserving detail and restraining noise are given by the proposed method. The decision makers can judge relatively and select one or more solutions according to the problem requirements. Figure optionsDownload as PowerPoint slide