Selective Transfer Based Evolutionary Multitasking Optimization for Change Detection

Change detection in multitemporal remote sensing images aims to generate a difference image (DI) and then analyze it to identify the unchanged/changed areas. The current change detection techniques always investigate a single change detection task of two images from the image series one by one and may ignore the relevant information across the different tasks. Furthermore, theoretical results have demonstrated that the distribution of DI can be interpreted by a Rayleigh-Rice mixture model (RRMM). The parameters of RRMM are usually estimated by the expectation-maximization (EM) algorithm, which is easy to be trapped into local minima. In order to address these issues, a selective transfer based evolutionary multitasking change detection method is proposed to deal with multiple change detection tasks concurrently. For each change detection task, the log-likelihood function and centroid distance function are considered as two objectives to be optimized simultaneously. In the proposed method, a RRMM parameter estimation driven initialization method with random partition of the data is designed by maximum likelihood estimates of the parameters. Then the next population is generated by the intra-task and inter-task genetic transfer operators. A selective knowledge transfer based local search strategy is proposed to further improve the population by applying EM algorithm. In this strategy, the samples in the unchanged class of multiple tasks are utilized to estimate the parameters to acquire knowledge transferred from the other task. Experiments on three real remote sensing data sets demonstrate that the selective transfer based evolutionary multitasking change detection method is able to accelerate the convergence and achieve superior performance in terms of accuracy.