高光谱图像波段选择的改进二进制布谷鸟算法

波段选择是高光谱遥感图像分类的重要前提，本文提出了一种用于高光谱遥感图像波段选择的改进二进制布谷鸟算法，通过使用混合二进制编码算法更新子代鸟巢和使用遗传算法交叉方式更新被发现鸟巢两个方面对二进制布谷鸟算法进行改进，找出在图像中起主要作用且相关性低的波段，实现对高光谱遥感图像降维。将本文算法运用于PaviaU数据集和AVIRIS数据集，并与二进制布谷鸟算法、二进制粒子群算法、最小冗余最大相关算法、Relief算法等进行对比分析。结果表明，改进二进制布谷鸟算法波段特征选择效率更高，且选取的波段更具代表性，能够较好地提高后续分类精度。

Improved binary cuckoo search algorithm for band selection in hyperspectral image

Spectral band selection serves as an important part in hyperspectral image classification. In this paper, an improved binary cuckoo search algorithm for band selection in hyperspectral image is proposed. Binary cuckoo search algorithm is improved by these two ways, one of which is that we update the nests of offspring by using a binary encoding algorithm. Another one is that the found nests are updated based on the crossover mode of genetic algorithm. The improved binary cuckoo search algorithm achieves the goal of dimensionality reduction of hyperspectral image by finding the bands with low correlation and the vital function in the image. The improved binary cuckoo algorithm is applied to PaviaU datasets and AVIRIS datasets, compared with binary cuckoo algorithm, binary particle swarm algorithm, minimum redundancy maximum correlation algorithm, relief algorithm. The results show that the improved binary cuckoo search algorithm is more efficient in the band selection, and the selected bands are more representative and can improve the precision of the image classification.