A Feature Selection Strategy for Hyperspectral Images Classification Based on GANBPSO-SVM
Xie Fuding;Yao Rao;College of Urban and Environment, Liaoning Normal University;
Rich spectral information from hyperspectral images can aid in the classification and recognition of the ground objects. Currently, hyperspectral images classification has already been applied successfully in various fields. However, the high dimensions of hyperspectral images cause redundancy in information and bring some troubles while classifying precisely ground truth. Hence, this paper proposes a hybrid feature selection strategy based on the Genetic Algorithm and the Novel Binary Particle Swarm Optimization(GANBPSO) to reduce the dimensionality of hyperspectral data while preserving the desired information for target detection and classification analysis. The proposed feature selection approach automatically chooses the most informative features combination. The parameters used in support vector machine(SVM) simultaneously are optimized, aiming at improving the performance of SVM. To show the validity of the proposal, Indian Pines(AVIRIS 92 AV3 C) data set which is widely used to test the performance of feature selection techniques is chosen to feed the proposed method. The obtained results clearly confirm that the new approach is able to automatically select the most informative features in terms of classification accuracy without requiring the number of desired features to be set a priori by users. Experimental results show that the proposed method can achieve higher classification accuracy than traditional methods.