Impurity function band prioritization based on particle swarm optimization and gravitational search algorithm for hyperspectral images

Modern satellite imaging technology has resulted in an increased number of hyperspectral bands acquired by state-of-the-art sensors. It significantly advances the field of remote sensing. Owing to the increasing number of bands, the huge data quantity causes the curse of dimensionality and leads to the worse accuracy. It also increases the computational complexity exponentially as the problem size increases. It's therefore important to reduce dimensionality in order to prevent the curse of dimensionality. In this paper, a novel dimensionality reduction, named impurity function band prioritization method based on the particle swarm optimization and the gravitational search algorithms, is proposed to reduce the number of hyperspectral bands. The experimental results show that our approach can efficiently reduce dimensionality of hyperspectral data sets and significantly achieve a better classification accuracy compared to other methods.