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

作     者：Jia, Heming Lang, Chunbo Oliva, Diego Song, Wenlong Peng, Xiaoxu 

作者机构：Northeast Forestry Univ Coll Mech & Elect Engn Harbin 150040 Heilongjiang Peoples R China Univ Guadalajara Dept Ciencias Computac CUCEI Ave Revoluc 1500 Guadalajara 44430 Jalisco Mexico 

出 版 物：《REMOTE SENSING》 (遥感)

年 卷 期：2019年第11卷第9期

页      面：1134-1134页

核心收录：

学科分类：0830[工学-环境科学与工程（可授工学、理学、农学学位）] 1002[医学-临床医学] 070801[理学-固体地球物理学] 07[理学] 08[工学] 0708[理学-地球物理学] 0816[工学-测绘科学与技术] 

基　　金：National Nature Science Foundation of China 

主　　题：satellite image image segmentation image thresholding hybrid optimization grasshopper optimization algorithm differential evolution minimum cross entropy 

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.