基于K-均值聚类与夹角余弦法的多光谱分类算法

近年来对高光谱与多光谱进行分类去混的研究方法很多,K-均值聚类算法与光谱相似度计算算法都属于成熟的分类算法.作者在对其研究基础上,将K-均值算法进行改进,并融入光谱相似度匹配算法,形成一种新的光谱分类算法,找出两条距离最远的光谱作为参考光谱,用欧氏距离法或夹角余弦法对数据立方体进行分类,并且从数据立方体中删除属于这两条谱线的其余谱线,同时找出与两条参考光谱距离最远或者夹角最大者作为第三条参考光谱,对剩余数据立方体进行新的分类,并在此算法上用多光谱数据立方体进行了试验验证.通过ENVI用K-均值(K-means)进行分类,与改进的K-means算法和夹角余弦法Matlab仿真结果进行比较,后两种对于两种气泡的分类效果都很好,对背景的分类改进的K-means算法效果较好,尤其是欧氏距离法能将背景完整地分离出来.

The multi-spectra classification algorithm based on K-means clustering and spectral angle cosine

The classification and de-aliasing methods with respect to multi-spectra and hyper-spectra have been widely studied in recent years. And both K-mean clustering algorithm and spectral similarity algorithm are familiar classification methods. The present paper improved the K-mean clustering algorithm by using spectral similarity match algorithm to perform a new spectral classification algorithm. Two spectra with the farthest distance first were chosen as reference spectra. The Euclidean distance method or spectral angle cosine method then were used to classify data cube on the basis of the two reference spectra, and delete the spectra which belongs to the two reference spectra. The rest data cube was used to perform new classification according to a third spectrum, which is the farthest distance or the biggest angle one corresponding to the two reference spectra. Multi-spectral data cube was applied in the experimental test. The results of K-mean clustering classification by ENVI, compared with simulation results of the improved K-mean algorithm and the spectral angle cosine method, demonstrated that the latter two classify two air bubbles explicitly and effectively, and the improved K-mean algorithm classifies backgrounds better, especially the Euclidean distance method can classify the backgrounds integrally.