胡梦林, 万幼川, 王明威, 高贤君, 高雄. 基于混沌杜鹃搜索算法的高光谱影像波段选择[J]. 微电子学与计算机, 2018, 35(4): 124-129.
引用本文: 胡梦林, 万幼川, 王明威, 高贤君, 高雄. 基于混沌杜鹃搜索算法的高光谱影像波段选择[J]. 微电子学与计算机, 2018, 35(4): 124-129.
HU Meng-lin, WAN You-chuan, WANG Ming-wei, GAO Xian-jun, GAO Xiong. Band Selection Based on Chaotic Cuckoo Search Algorithm for Hyperspectral Image[J]. Microelectronics & Computer, 2018, 35(4): 124-129.
Citation: HU Meng-lin, WAN You-chuan, WANG Ming-wei, GAO Xian-jun, GAO Xiong. Band Selection Based on Chaotic Cuckoo Search Algorithm for Hyperspectral Image[J]. Microelectronics & Computer, 2018, 35(4): 124-129.

基于混沌杜鹃搜索算法的高光谱影像波段选择

Band Selection Based on Chaotic Cuckoo Search Algorithm for Hyperspectral Image

  • 摘要: 针对高光谱遥感影像的波段降维问题, 结合杜鹃搜索算法良好的全局寻优能力和混沌映射局部寻优能力强的特点, 提出一种基于混沌杜鹃搜索算法的高光谱影像波段选择方法, 并对HYDICE影像进行仿真实验.同时进行该算法与遗传算法、粒子群算法和基本杜鹃搜索算法的对比实验, 实验结果表明该算法搜索能力更强, 最终所选波段子集的分类精度更高.

     

    Abstract: A band selection technique based on chaotic cuckoo search algorithm is proposed in this paper, which combined with cuckoo search algorithm and chaotic map to solve the problem of dimension reduction of hyperspectral image. As a common used evolutionary algorithm, cuckoo search algorithm has the better global optimization ability, and chaotic map can improve the local optimization ability of evolutionary algorithm. Hence, a HYDICE image is utilized to test the efficiency of the proposed method. experimental results demonstrate that the search ability of chaotic cuckoo search algorithm is better than genetic algorithm, particle swarm optimization algorithm, and standard cuckoo search algorithm. Meanwhile, the classification accuracy could be increased by using the selected band subset.

     

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