Adaptive Multiobjective Memetic Fuzzy Clustering Algorithm for Remote Sensing Imagery

Abstract

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.