An initialization friendly Gaussian mixture model based multi-objective clustering method for SAR images change detection

Speckle noise is a main obstacle for change detection tasks of synthetic aperture radar (SAR) images. However, change detection methods often focus on removing noise and ignore the importance of preserving details of SAR images, which results in a loss of classification accuracy. In order to alleviate the contradiction between removing noise and preserving details, a multi-objective change detection method based on a modified Gaussian mixture model (GMM) is proposed in our paper. In the framework of our multi-objective model, one objective is composed of an special GMM of being applied to distinguish changed and unchanged regions and preserve details. The another is a carefully crafted noise reduction operator for removing speckle noise, which is constructed as a penalty to refine the result of classification. The aforementioned conflicting objectives are optimized simultaneously, which is conducted by a common multi-objective evolutionary algorithm based on decomposition (MOEA/D). Then, a series of solutions are obtained, all of which represent a balance of detail preservation and noise reduction. Compared with three classical change detection methods, the proposed method is applied to four real datasets in experimental studies. Experimental results and theoretical analysis reveal its effectiveness.