Image segmentation based on fuzzy clustering with cellular automata and features weighting

Abstract

Aiming at the sensitivity of fuzzy C-means (FCM) method to the initial clustering center and noise data, and the single feature being not able to segment the image effectively, this paper proposes a new image segmentation method based on fuzzy clustering with cellular automata (CA) and features weighting. Taking the gray level as the object and combining fully the image feature and the spatial feature weighting and FCM, this paper quickly realizes the fuzzy clustering of the images segmentation by the CA’s self-iteration function and finally discusses the effectiveness and feasibility of the proposed method in long-term sequences satellite remote sensing image segmentation. Our experiments show that the proposed method not only has fast convergence speed, strong anti-noise property, and robustness, but also can effectively segment common images and long-term sequence satellite remote sensing images and has good applicability.