Adaptive Multistrategy Particle Swarm Optimization for Hyperspectral Remote Sensing Image Band Selection

Hyperspectral remote sensing band selection picks out characteristic feature combinations to weaken the strong correlation caused by spectral continuity. However, it is difficult for traditional methods with fixed strategies to search the entire space and make adjustments for the optimization process. Thus, the solutions obtained can be mostly local optima. In this article, a novel adaptive multistrategy particle swarm optimization (PSO) for hyperspectral image remote sensing band selection (AMSPSO_BS) is introduced to obtain a subset solution suitable for classification. The problem is modeled as an effective fitness function, and the quotient of the linear discriminant value and the mean mutual information (LD/MMI) is used to remove the redundancy between bands. The randomly generated solutions are then encoded to form a population, which rely on various particle update strategies (PUS) with different reference positions for updating. During the particle motion, the effect of each strategy on population evolution is considered comprehensively and reflected in the change of selection probability. And the motion parameters are dynamically adjusted to balance the global and local capabilities. Four hyperspectral remote sensing image datasets were utilized to conduct band selection experiments, to confirm the effectiveness of AMSPSO_BS.

Automated summary

This article introduces a novel adaptive multistrategy particle swarm optimization (AMSPSO) method for hyperspectral image remote sensing band selection. The method utilizes the quotient of linear discriminant value and mean mutual information to remove redundancy between bands. By dynamically adjusting the motion parameters, the method is able to balance global and local capabilities.