为降低高光谱数据量及计算复杂度，避免后续分类中的Hughes 现象，提出一种基于烟花算法降维的高光谱图像分类方法． 烟花算法采用类内紧密性系数与类间分离性
系数的加权和作为波段选择的度量准则，通过在高光谱数据空间内进行搜索，不断更新直至收敛，从而获得最优波段组合． 基于印第安纳数据集( AVIＲIS) 和帕维亚大学数据集( ＲOSIS) 数据对烟花算法、遗传算法和禁忌搜索算法进行波段选择的仿真实验，结果表明: 烟花算法选择出的波段组合数目相对较少，具有较低的算法复杂度，减少了耗时; 利用获得的波段组合进行高光谱图像分类时，与遗传算法、禁忌搜索算法的波段选择方法相比，文中所提方法在总体分类精度和Kappa 系数上分别提高0. 06% ～ 4. 72% 和0． 00 ～0. 09，可以得到令人满意的分类结果．

In order to reduce the size and computational complexity of hyperspectral data as well as to avoid Hughes phenomenon in the following classification,a novel hyperspectral image dimensionality reduction method on the basis of fireworks algorithm ( FWA) is proposed.In this method,the weighted sum of compactness coefficient and separation coefficient is used as the criterion for band selection,and a search is constantly performed and updated until the algorithm is convergent.Thus,an optimal band combination is successfully obtained.Then,a comparison among the dimensionality reduction methods respectively based on FWA,genetic algorithm ( GA) and tabu search ( TS) algorithm is made with AVIRIS and ROSIS datasets.The results indicate that FWA-based method is of lower computational complexity and smaller time consumption because it helps select fewer band combinations; and that,when the obtained band combination is used for hyperspectral image classification,the proposed FWA-based method is the best because it improves classification accuracy and Kappa coefficient by 0. 06% ～ 4. 72% and 0. 00 ～ 0. 09,respectively.