基于黄金正弦混沌斑鬣狗优化算法的高光谱波段选择  

Hyperspectral Wave Band Selection Based on Golden Sine and Chaotic Spotted Hyena Optimizer Algorithm

作　　者：戴天虹[1] 孙春雪 黄建平 谢千程 丛士杰 黄新望 李克新 Dai Tianhong;Sun Chunxue;Huang Jianping;Xie Qiancheng;Cong Shijie;Huang Xinwang;Li Kexin(College of Mechanical and Electrical Engineering,Northeast Forestry University,Harbin 150040,Heilongjiang,China;College of Artificial Intelligence,Wuxi Vocational College of Science and Technology,Wuxi 214000,Jiangsu,China)

机构地区：[1]东北林业大学机电工程学院,黑龙江哈尔滨150040 [2]无锡科技职业学院人工智能学院,江苏无锡214000

出　　处：《激光与光电子学进展》2022年第10期509-518,共10页Laser & Optoelectronics Progress

基　　金：中央高校基本科研业务费专项资金(2572019CP17,2572019CP19);黑龙江省自然科学基金(C201414,TD2020C0001);哈尔滨市科技创新人才项目(2014RFXXJ086)。

摘　　要：波段选择是降低高光谱数据维度,减少数据过多冗余的有效手段,是高光谱影像像素分类的重要前提。其本质上是一个复杂的组合优化问题,用传统的搜索方法不易得到满意的解。针对上述问题,提出了一种结合黄金正弦和混沌斑鬣狗算法(GSSHO)的高光谱波段选择方法。首先,使用混沌策略初始化斑鬣狗种群,提高种群的随机性和多样性;然后,用黄金正弦算法改进原始斑鬣狗算法搜索个体位置更新方式,提高算法的全局搜索能力;最后,设计结合分类精度和波段个数的适应度函数,对算法优化性能进行评价。在高光谱遥感数据集上,将该方法与其他先进优化算法进行比较,实验结果表明,该方法所选波段个数接近原波段的1/10,对于Pavia Centre数据集分类精度高达99.08%,优于其他对比方法,能以更合理的收敛方向找到最优解,所选波段数更少,分类精度更高,是一种高效的波段选择方法。Wave band selection is an effective means to reduce the dimension and much redundancy of hyperspectral data, and it is an important prerequisite for pixel classification of hyperspectral images. It is a complex combinatorial optimization problem in essence, and it is difficult to get satisfactory solution by traditional search methods. To solve the above problems, a method of hyperspectral band selection based on golden sine and chaotic the spotted hyena optimization algorithm(GSSHO) is proposed. Firstly, chaos strategy is used to initialize the spotted hyena population to improve the randomness and diversity of the population. Secondly, golden sine algorithm is used to improve the original spotted hyena optimization(SHO) algorithm to search individual position update mode to improve the global search ability of the algorithm. Finally, a fitness function combining classification accuracy and band number is designed to evaluate the optimization performance of the algorithm. On hyperspectral remote sensing data sets, this method is compared with other advanced optimization algorithms. The experimental results show that the number of bands selected in this algorithm is close to one tenth of the original band, and the classification accuracy of Pavia Centre data set is up to 99. 08%, which is better than those of other comparison methods. It can find the optimal solution with more reasonable convergence direction, and the number of selected bands is less, and the classification accuracy is higher. It is an efficient method for selecting wave band.

关 键 词：成像系统 波段选择 斑鬣狗优化算法 黄金正弦算法 混沌策略 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置]