Evolutionary Multiobjective Change Detection via Self-paced Learning and Fuzzy Clustering

Fuzzy clustering algorithm based on multiobjective optimization can achieve accurate and comprehensive clustering results. However, the estimation of objective values for this multiobjective optimization problem (MOP) might be expensive. Offspring's selection driven by simple evaluation is time consuming. Therefore, we integrate regression techniques to determine the superiority of the offspring solutions in the evolution process. However, it suffers from an issue that it is hard to collect reliable samples to train such a robust regression model. In this paper, an evolutionary multiobjective fuzzy clustering method via self-paced learning is proposed for change detection. In the proposed method, the self-paced learning process is implemented to collect reliable training samples for training a robust regression model, which can help to select promising offspring solutions from the candidate solutions for MOP. Experiments on three remote sensing image datasets demonstrate that the proposed method can significantly outperform those state-of-art methods for change detection in terms of accuracy and robustness.