Improvement SVM Classification Performance of Hyperspectral Image Using Chaotic Sequences in Artificial Bee Colony

Artificial bee colony algorithm is an effective algorithm for parameter optimization, but the traditional artificial bee colony algorithm is liable to fall into local extreme points at a later stage. In this paper, we propose an improved artificial bee colony optimization algorithm, which solves the problems of premature convergence and falling into the local extreme value in the classification of hyperspectral images. First we use an improved chaotic sequence with higher randomness to initialize and update nectar sources to expand the distribution of nectar sources. Secondly, the optimized adaptive step size is introduced into the neighborhood search to speed up the algorithm convergence and improve the search efficiency. Then we build an improved artificial bee colony algorithm support vector machine optimization model to mine the optimal values of penalty factor
$C$
and kernel function parameter
$\sigma $
. Next, the model was used to perform classification experiments on two hyperspectral images (University of Pavia, Indian Pine) with different attributes, and compared with the traditional bee colony algorithm, genetic algorithm, and particle swarm algorithm. Experimental results on HSI datasets demonstrate the superiority of the proposed method over several well-known methods in both classification accuracy and convergence speed.