A Customized Particle Swarm Optimization for Classification of Multispectral Imagery Based on Feature Fusion

An attempt has been made in this paper to classify multispectral images using customized particle swam optimization. To reduce the time consumption due to increase in dimensionality of multispectral imagery a preprocessing is done using feature extraction based on decision boundary. The customized particle swam optimization then works on the reduced multispectral imagery to find globally optimal cluster centers. Here particle swam optimization is tailored for classification of multispectral images as customized particle swam optimization. The modifications are performed on the velocity function such that velocity in each iteration is updated as a factor of g-best (global best) alone and the particle structure is made to incorporate the entire cluster centers of the reduced imagery. The initialization of particles is accomplished using modified k-means in order to retain the simplicity. AVIRIS images are used as test site and it was found that the customized particle swam optimization finds the globally optimal clusters with 98.56% accuracy.