Abstract
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 σ. 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.
Highlights
With the development of remote sensing technology, people can extract more useful data and information from hyperspectral images (HSI), and image classification is an important means of obtaining information
Group intelligence algorithm is widely used in SVM parameter optimization because it does not need to traverse each position in the solution space, such as genetic algorithm (GA) [17], ant colony algorithm(ACO) [18], particle swarm algorithm (PSO) [19], [20]
The improved bee colony algorithm is applied to mining the penalty parameter C and kernel parameters of the SVM to form the IABC-SVM optimization model
Summary
With the development of remote sensing technology, people can extract more useful data and information from hyperspectral images (HSI), and image classification is an important means of obtaining information. C. Zhao et al.: Improvement SVM Classification Performance of HSI Using Chaotic Sequences in Artificial Bee Colony. Artificial Bee Colony Algorithm (ABC) [21], proposed by Karaboga, is a group intelligence algorithm following the above algorithm [22], [23], which has the advantages of simplicity, flexibility and robustness. In order to solve the problem of optimizing SVM parameters by artificial bee colony algorithm, we improved chaotic mapping to generate chaotic sequences, expand the distribution range of honey sources, and jump out of the local optimum. (3) Design an optimization model based on improved artificial bee colony algorithm support vector machine (IABC-SVM).
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