基于遗传和蚁群组合算法优化的遥感图像分割
Segmentation of Remote Sensing Image Based on a Combination of Genetic Algorithm and Ant Colony Algorithm
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摘要: 将遗传算法和蚁群算法组合对模糊聚类进行优化,巧妙地对图像的像素特征和空间特征进行提取,利用这些特征作为聚类依据,将图像的多个特征结合到智能计算中,充分利用了遗传算法和蚁群算法各自的优势和特点,既提高了图像分割的准确性,又加快了分割过程的速度。实验结果表明,遗传算法和蚁群组合算法优化的模糊聚类是一种性能良好的遥感图像分割方法。Abstract: We combine genetic algorithm(GA) with ant colony algorithm(ACA),which is introduced to optimize fuzzy cluster.Both pixel features and spatial features are extracted subtly,which are all used as cluster basis in intelligent computation.Niche genetic algorithm is used to optimize the course of looking for multiple optimal cluster centers.ACA is used to optimize the course of fuzzy clusters.In this way,the accuracy of image segmentation is improved greatly,and the course of image segmentation is accelerated as well.Experimental results show that the method proposed this paper is an efficient approach to segmentation of remote sensing image.
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