A novel fast image segmentation algorithm for large topographic maps

摘要: Image segmentation for topographic maps is challenging due to their low quality, high degrees of mixed and false coloring. Besides, many pixels cannot be explicitly separated from each other because the fuzziness colors. Therefore algorithms based on fuzzy theory are suitable process such images utilizing ability deal with blurring effect. However, there still some problems large-scale data, time complexity inaccurate classification. In order overcome these problems, we propose a novel algorithm segmenting large ideas theory, randomized sampling multilevel image fusion. this algorithm, map randomly sampled first. Then optimal clustering centers acquired by C-means (FCM) clustering. Further, segmented classification method. Finally, fusion used fuse into final maps. Randomized reduce amount improve efficiency segmentation. Multilevel can make more accurate. The experiments show that our method has higher accuracy than traditional ones. It provides reliable