Genetically derived fuzzy c-means clustering algorithm for segmentation

The proper classification of pixels is an important step in the realm of satellite imagery, to partition different land cover regions. This paper describes a clustering method that utilizes hard and fuzzy clustering algorithms. The performance of the algorithm is optimized using genetic algorithm, which searches the best cluster centers to initialize the fuzzy partition matrix in place of random initialization. The proposed approach provides accurate clustering results for gray-level images. Comparison between segmentation results of hard c-means, fuzzy c-means and fuzzy c-means genetic algorithm (FGA) is presented.