蝙蝠优化的二维Tsallis熵多阈值SAR图像分割

针对智能优化SAR图像分割算法存在计算量大、易陷入局部最优、分割精度不够等问题，融合蝙蝠算法和二维Tsallis熵多阈值，提出了一种蝙蝠优化的二维Tsallis熵多阈值SAR图像分割算法。算法利用立方映射均匀化初始蝙蝠种群，引入Levy飞行特征加强算法跳出局部最优能力，使用Powell局部搜索加快算法收敛等3方面改进蝙蝠算法；同时将二维Tsallis熵单阈值分割方法扩展到多阈值分割，建立基于多阈值的选取方法，并结合改进的蝙蝠算法，将二维Tsallis熵多阈值应用于SAR图像分割中。仿真结果表明，与其他智能优化分割算法相比，本分割算法在边缘处理和分割精度上都有明显优势。

Bat Optimized Two-Dimensional Tsallis Entropy Multi-Threshold SAR Image Segmentation

For intelligent optimization of SAR image segmentation algorithm, there are some problems, such as large computation, local peak, and insufficient segmentation accuracy. In this paper, a SAR image segmentation algorithm is proposed, which combines bat algorithm and two-dimensional Tsallis entropy multi-threshold. The algorithm homogenizes the initial bat population by using cubic mapping, introduces the Levy flight feature enhancement algorithm to jump out of the local optimum capability, and improves the bat algorithm in three aspects by using Powell local search and accelerating algorithm convergence. The algorithm simultaneously extends the two-dimensional Tsallis entropy single-threshold segmentation method to multi-threshold segmentation and establishes a multi-threshold based selection method. Combined with improved bat algorithm, two-dimensional Tsallis entropy multi-threshold is applied to SAR image segmentation. Simulation results show that the segmentation algorithm has obvious advantages in edge processing and segmentation accuracy compared with other intelligent optimization algorithms.