Research on K Means Clustering Parallel Algorithm of Remote Sensing Image
Remote sensing image oriented K Means algorithm is one of important unsupervised clustering algorithms using no former knowledge and has been widely used in remote sensing image analysis. It is useful but hard to parallelize K Means algorithm because of its algorithm complexity and unique process procedure. This paper makes a study on parallel processing of K Means algorithm and a new strategy is proposed to improve processing speed and accuracy. This strategy is based on the block approaching algorithm parallel model, and gets a good tradeoff between efficiency and accuracy. Based on the experiments,proposal for further research on iterative algorithms is provided.