Parallel implementation of neighbor-based FCM clustering for remote sensing image

Considering the spatial relationship of pixels when it is used in classification for remote-sensing imagery,Neighbor-based FCM algorithm was put forward by modifying the value of fuzzy membership degree with the neighbor information during the clustering iterations.We use dominant class,if it can be determined in a fixed neighbor region,or the weighted parameters based on the distance of neighbors to perfect the membership degree of central pixel.Then parallel implement for the algorithm was also proposed by taking account of the communication complexity and the spatial relationship for image partition.In the end,the experimental data indicate the efficiency of the algorithm in decreasing the clustering iterations and increasing the classified precision,and the parallel algorithm also achieves the satisfying linear speedup.