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摘要： 目前，高光谱遥感影像分类时光谱信息使用较多，难以充分挖掘空间信息。针对该问题，提出一种基于改进萤火虫算法(Firefly Algorithm，FA)的高光谱遥感多特征优化方法。首先提取高光谱遥感影像的4种空间特征：局部统计特征、灰度共生矩阵特征、Gabor特征和形态学特征，并与波段选择的部分光谱波段组合，构建多特征集合；然后利用萤火虫算法对提取的多特征集合进行优化和特征选择，针对萤火虫算法收敛速度较慢问题，借鉴粒子群优化算法，引入随机惯性权重改进了萤火虫算法的位置更新公式；目标函数采用JM距离(Jeffreys-Matusita Distance)和Fisher Ratio。利用两组城市高光谱遥感数据进行了土地覆被分类研究，并将仅利用原始光谱信息进行波段选择的分类结果与利用多特征信息的分类结果进行了对比分析。实验表明：随机惯性权重可以提高FA特征选择的速度，且优化后的光谱与空间信息特征有助于提高城市土地覆被分类的精度，两组实验数据的特征优选结果统计均表明空间特征中的形态学特征被选择次数最多，局部统计特征和形态学特征相对于GLCM特征和Gabor特征更有助于高光谱遥感图像的分类。  关键词： 高光谱遥感;  多特征优化;  特征选择;  萤火虫算法     Abstract: The utilization of hyperspectral remote sensing image is mainly based on the spectral information，and the spatial information is always be ignored.To solve this problem，a novel hyperspectral multiple features optimization approach based on improved firefly algorithm is presented.Firstly，four spatial features，the local statistical features，gray level co-occurrence matrix features，Gabor filtering features and morphological features of hyperspectral remote sensing image are extracted，and some spectral bands are selected and then combined with these spatial features，and the feature set is constructed.Then，the firefly algorithm is used to optimize the extracted features.In view of the slow convergence speed of firefly algorithm，we use the random inertia weight from particle swarm optimization algorithm to modifiy the location update formula of firefly algorithm，and JM(Jeffreys-Matusita)distance and Fisher Ratio are used as the objective function.Two urban hyperspectral datasets are used for performance evaluation，and the classification results derived from spectral information and spectral-spatial information are compared.The experiments show that random inertia weight can improve the speed of FA-based feature selection algorithm，the performance with multiple features is better than that of spectral information for urban land cover classification，The statistical results of the two sets of experimental data indicate that the selected number of morphological features are the most in the four spatial features.The local statistical features and morphological features are more helpful to the classification of hyperspectral remote sensing images than GLCM and Gabor features.

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