基于鲸鱼优化和模糊聚类的林火图像分割

Forest Fire Image Segmentation Based on Whale Optimization and Fuzzy Clustering

鲸鱼优化算法(Whale Optimization Algorithm,WOA)收敛速度快、精度高,在寻找最优解的过程中效率较高,对其进行一定的改进,利用改进的鲸鱼优化算法来优化核模糊聚类算法中聚类中心的生成过程,提出一种基于改进鲸鱼优化的核模糊C均值聚类算法(Impoved Whale Optimization Fuzzy C-Means Clustering,IWOA_KFCM)。为了验证算法的性能,采用定性和定量两种分析方法对基于改进鲸鱼的核模糊聚类算法进行了测评,实验结果证明改进鲸鱼的核模糊聚类算法具有较高的聚类准确率,同时时间效率也比较高。将基于改进鲸鱼的核模糊聚类算法应用于林火图像的分割问题上,获得了更为准确的分割阈值和更高的分割效率,具有较强的工程实用性。

Whale optimization algorithm(WOA)has the features of fast convergence speed,high precision,and high effi ciency in the process of fi nding the optimal solution.When improved to some extent,whale optimization algorithm can be used to optimize the generation process of cluster center in the kernel fuzzy clustering algorithm;thus,the impoved Whale optimization Fuzzy C-means Clustering algorithm(IWOA_KFCM)is proposed.In order to verify the performance of the algorithm,qualitative and quantitative analysis methods are used to evaluate the kernel fuzzy clustering algorithm based on the improved whale.The experimental results show that the improved whale kernel fuzzy clustering algorithm has high clustering accuracy and high time effi ciency.The kernel fuzzy clustering algorithm based on the improved whale can be applied to the segmentation of forest fi re image,and has strong engineering practicability,because it can obtain more accurate segmentation threshold and higher segmentation effi ciency.