Comparative Study of the Effect of Different Fitness Functions in PSO Algorithm on Band Selection of Hyperspectral Imagery

The innate intricacy of hyperspectral images and the absence of the mark data set make the band selection a challenging task in hyperspectral imaging. Computational multifaceted nature can be decreased by distinguishing suitable bands and simultaneously optimizing the number of bands. The PSO (Particle swarm optimization) based technique is used for this purpose. Fitness function takes a significant role in PSO to make a balance between the optimal solution and the accuracy rate. Different distance metrics like Euclidean, City Block, etc. are used as fitness functions and the aftereffects of a similar investigation on different data sets are reported in the present paper.