Hybrid Grasshopper Optimization Algorithm and Differential Evolution for Multilevel Satellite Image Segmentation

Remote Sensing

Heming Jia¹  Xiaoxu Peng¹  Wenlong Song¹  Chunbo Lang¹  Diego Oliva² 

[1] College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China;Depto. de Ciencias Computacionales, Universidad de Guadalajara, CUCEI, Av. Revolución 1500, Guadalajara 44430, Mexico;

关键词: satellite image;    image segmentation;    image thresholding;    hybrid optimization;    grasshopper optimization algorithm;    differential evolution;    minimum cross entropy;   

DOI  :  10.3390/rs11091134

来源: DOAJ

【 摘 要 】

An efficient satellite image segmentation method based on a hybrid grasshopper optimization algorithm (GOA) and minimum cross entropy (MCE) is proposed in this paper. The proposal is known as GOA–jDE, and it merges GOA with self-adaptive differential evolution (jDE) to improve the search efficiency, preserving the population diversity especially in the later iterations. A series of experiments is conducted on various satellite images for evaluating the performance of the algorithm. Both low and high levels of the segmentation are taken into account, increasing the dimensionality of the problem. The proposed approach is compared with the standard color image thresholding methods, as well as the advanced satellite image thresholding techniques based on different criteria. Friedman test and Wilcoxon’s rank sum test are performed to assess the significant difference between the algorithms. The superiority of the proposed method is illustrated from different aspects, such as average fitness function value, peak signal to noise ratio (PSNR), structural similarity index (SSIM), feature similarity index (FSIM), standard deviation (STD), convergence performance, and computation time. Furthermore, natural images from the Berkeley segmentation dataset are also used to validate the strong robustness of the proposed method.

【 授权许可】

Unknown