Adaptive Multiobjective Memetic Fuzzy Clustering Algorithm for Remote Sensing Imagery


Due to the intrinsic complexity of remote sensing images and the lack of prior knowledge, clustering for remote sensing images has always been one of the most challenging tasks in remote sensing image processing. Recently, clustering methods for remote sensing images have often been transformed into multiobjective optimization problems, making them more suitable for complex remote sensing image clustering. However, the performance of the multiobjective clustering methods is often influenced by their optimization capability. To resolve this problem, this paper proposes an adaptive multiobjective memetic fuzzy clustering algorithm (AFCMOMA) for remote sensing imagery. In AFCMOMA, a multiobjective memetic clustering framework is devised to optimize the two objective functions, i.e., Jm and the Xie-Beni (XB) index. One challenging task for memetic algorithms is how to balance the local and global search capabilities. In AFCMOMA, an adaptive strategy is used, which can adaptively achieve a balance between them, based on the statistical characteristic of the objective function values. In addition, in the multiobjective memetic framework, in order to acquire more individuals with high quality, a new population update strategy is devised, in which the updated population is composed of individuals generated in both the local and global searches. Finally, to evaluate the proposed AFCMOMA algorithm, experiments using three remote sensing images were conducted, which confirmed the effectiveness of the proposed algorithm.