Remote sensing imagery clustering using an adaptive bi-objective memetic method

Due to the intrinsic complexity of the remote sensing image and the lack of the prior knowledge, clustering for remote sensing image has always been one of the most challenging works in remote sensing image processing. The proposed algorithm constructs a bi-objective memetic-based framework, exploiting the feature space more efficiently. In the framework, two objective functions, Jm and XB, are used as the objective functions for bi-objective optimization. Furthermore, an adaptive local search method which can dynamically adjust its parameter value according to the selection probability has been developed and incorporated into the proposed algorithm. In order to speed the convergence and obtain more non-dominated solutions in the Pareto front, a new strategy is newly devised in the local search process, which considers more solutions as the candidate for the next generation. To evaluate the proposed algorithm, some experiments on two multi-spectral images are conducted. The results show that the proposed algorithm can achieve better performance, compared with related methods.