Hyperspectral image classification based on Multiple Improved particle swarm cooperative optimization and SVM

The huge increase of hyperspectral data dimensionality and information redundancy has brought high computational cost as well as the over-fitting risk of classification. In this paper, we present an automatic band selection and classification method based on a novel wrapper Multiple Improved particle swarm cooperative optimization and support vector machine model (MIPSO-SVM). The MIPSO-SVM model optimizes both the band subset and SVM kernel parameters simultaneously. In the proposed model, the particle swarm is divided into two sub-swarms. And PSO is improved firstly, by the new update strategy of position and velocity. Then the sub-swarms perform the improved PSO (IPSO) for band selection and classifier parameters optimization independently. Finally, in the process of cooperative evolution, extremal optimization (EO) is incorporated to maintain the diversity of swarms and enhance the space exploration ability of the proposed model. Experimental results demonstrate the effectiveness of the proposed method for band selection and classification of hyperspectral images.