基于改进遗传算法的超光谱图像特征选择方法

提出的特征选择新方法充分利用遗传算法并行搜索、全局寻优的优点，并结合超光谱图像特征选择的具体应用，选择表征类别可分性的判别标准作为评价函数计算个体适应度，通过交叉和变异操作实现个体进化．为加快算法收敛速度，提高遗传算法性能，在遗传算法中引入了两代竞争机制，获取最佳的分类特征组合．利用一幅200波段的AVIRIS超光谱图像进行的仿真实验结果表明，所提出的方法用于特征选择具有分类精度高，计算耗时少的优点．

A feature selection method based on modified genetic algorithm with hyperspectral images

A new method based on Genetic Algorithm (GA) is proposed for feature selection of hyperspectral images. The proposed method fully uses the merit of genetic algorithm in parallel search and global optimization in terms of the application of feature selection of hyperspectral images. It exploits criterions that represent class separability to implement the individual evolution through crossover and mutation. To accelerate convergence and improve its performance, we introduce competition between two generations to simple genetic algorithm, and obtain the optimal combination of features for classification. The numerical experiments are performed on hyperspectral data with 200 bands collected by Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). The experimental results show that the proposed method has high classification accuracy and low computation cost for feature selection.