Fuzzy C-Means Clustering Based on Improved Marked Watershed Transformation
Abstract: Currently, the fuzzy
c-means algorithm plays a certain role in remote sensing image classification.
However, it is easy to fall into local optimal solution, which leads to poor
classification. In order to improve the accuracy of classification, this paper,
based on the improved marked watershed segmentation, puts forward a fuzzy
c-means clustering optimization algorithm. Because the watershed segmentation
and fuzzy c-means clustering are sensitive to the noise of the image, this
paper uses the adaptive median filtering algorithm to eliminate the noise
information. During this process, the classification numbers and initial
cluster centers of fuzzy c-means are determined by the result of the fuzzy
similar relation clustering. Through a series of comparative simulation
experiments, the results show that the method proposed in this paper is more
accurate than the ISODATA method, and it is a feasible training method.
Keywords: adaptive median
filtering, marked watershed segmentation, fuzzy similarity relation, fuzzy
CMeans clustering
Author: Cuijie Zhao, Hongdong
Zhao, Wei Yao
Journal Code: jptkomputergg160231
