Hyperspectral Multiple Features Optimization Using Improved Firefly Algorithm

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.