Abstract:

In order to solve the problem of selecting the clustering number of remote sensing images and positions of center points, Proposed a PCVOS ( Parallelized Center Vector Optimized Selection Algorithm for Remote Sensing Image Cluster) which introduces fuzzy evaluation of the objective function and put forward an evaluation mechanism of chromosomes to improve the diversity of category and space division of clustering chromosomes is proposed. The MPI multi-process parallel technology is simultaneously introduced to speed up the running speed of the algorithm. The experiment shows that compared with the traditional K-Means, ISODATA(Iterative Self Organizing Data Analysis Techniques Algorithm) and ACDE ( Automatic Clustering Differential Evolution) algorithm, PCVOS can obtain a better clustering effect, and make full use of parallel resources to speed up the running speed of the algorithm.

Key words: cluster, remote sensing image, category number, parallel computing, optimized selection