【摘要】 为解决林火图像传统阈值分割方法时效性差、分割精度低等问题，提出一种基于多策略融合未来搜索算法(IFSA)的多阈值林火图像分割方法。在提升算法的性能方面，采用帐篷映射(Tent映射)初始化种群中的个体，引入自适应权重与认知因子增强种群内部信息交流，并对最优位置引入柯西分布与高斯分布结合的变异机制提高算法的收敛精度。利用改进算法对森林火灾图像进行分割，并选取最佳适应度、峰值信噪比和结构相似度作为评价指标，与粒子群优化算法、灰狼优化算法等进行对比分析。研究结果表明，改进的未来搜索算法(Improved Future Search Algorithm, IFSA)的适应度曲线收敛效果明显优于其他对比算法，峰值信噪比、结构相似度取得最优的实验次数分别占总实验次数的100%与91.67%,证明基于IFSA的图像分割方法能有效改善林火图像分割效果，为林火特征的提取与分析建立依据。更多还原

【Abstract】 A multi-threshold forest fire image segmentation method based on a multi-strategy fused future search algorithm(IFSA) is proposed to address the shortcomings of traditional threshold segmentation methods for forest fire images, such as poor timeliness and low segmentation accuracy. To improve the performance of the algorithm, the solution space is initialized using Tent chaotic mapping. Inertia weights and cognitive factors are introduced to enhance exchange within populations. A variational mechanism combining the Cauchy distribution and Gaussian distribution is introduced to improve the convergence accuracy of the algorithm. The improved algorithm is used to segment forest fire images, and the best adaptation, peak signal-to-noise ratio and structural similarity are selected as evaluation metrics and compared with particle swarm optimization algorithm and grey wolf optimization algorithm for analysis. The results show that the convergence of the adaptation curve of improved future search algorithm(IFSA) is better than that of other algorithms, and the number of experiments in which the peak signal-to-noise ratio and structural similarity achieved the best results accounted for 100% and 91.67% of the total number of experiments, respectively. It is proved that the IFSA-based image segmentation method can effectively improve the segmentation effect of forest fire images and establish a basis for the extraction and analysis of forest fire features.更多还原