摘要
光谱反射率重建过程中,训练样本的选择方法及样本容量与重建精度密切相关,寻找一种高效的训练样本选择方法是光谱重建的目标之一。K均值聚类计算复杂度小,计算效率高,但因聚类初始值选择的随机性,以及离群点的影响致使聚类结果不稳定,进而影响光谱重建的精度。基于此,提出了一种改进K均值聚类的训练样本选择方法。首先,将训练样本集的几何中心作为聚类中心的初始值;其次,基于高斯函数构建样本空间分布概率密度函数,并以欧几里德(欧式)距离作为其他聚类中心的度量依据;最后,在训练样本集中,基于簇内平方差度量光谱反射率样本间的相似度,将每个聚类子集中与中心距离最近的样本作为训练样本。为验证该方法的有效性,通过主成分分析法进行光谱重建。实验结果表明,所提的方法相较于传统的方法,光谱重建精度有一定的提高,重建光谱的平均均方根误差小于4%, CIE DE2000色差小于3.756 7。提出的改进的K均值聚类的训练样本选择方法,能够一定程度上提高了光谱重建精度,基本满足复制再现图像的要求。
Developing an efficient training sample selection method is one of the goals of spectral reflectance reconstruction.In spectral reflectance reconstruction,the training set selection method and sample capacity are strongly related to the reconstruction accuracy.The accuracy of the spectral reconstruction is affected by the unstable clustering results due to the randomness of the starting value selection and the outliers.K-means clustering has low computing complexity and great computational efficiency.Based on this,this paper proposes an improved K-means clustering training sample selection method.Firstly,the geometric center of the training sample set is used as the initial value of the clustering center;secondly,the probability density function of the spatial distribution of the samples is constructed based on the Gaussian function,and the Euclidean(Euclidean)distance is used as the measure of other clustering centers;finally,the similarity between the spectral reflectance samples in the training sample set is measured based on the intra-cluster squared difference,and the sample with the closest distance to the center in each clustering subset is used as the training samples are used to verify the effectiveness of the method.The spectral reconstruction was performed by principal component analysis.The experimental results show that the proposed method has been improved significantly compared with the traditional method,the average root-mean-square error of the reconstructed spectra is less than 4%,and the CIEDE2000 color difference is less than 3.7567.The improved training sample selection method of K-mean clustering proposed in this paper can improve the spectral reconstruction accuracy to some extent and meet the requirements of reproducing the reproduced images.
作者
刘振
刘莉
樊硕
赵安然
刘思鲁
LIU Zhen;LIU Li;FAN Shuo;ZHAO An-ran;LIU Si-lu(School of Communication,Qufu Normal University,Rizhao 276800,China;School of Engineering,Qufu Normal University,Rizhao 276800,China)
出处
《光谱学与光谱分析》
SCIE
EI
CAS
CSCD
北大核心
2024年第1期29-35,共7页
Spectroscopy and Spectral Analysis
基金
国家自然科学基金项目(61405106)
山东省自然科学基金项目(ZR2020MF125)资助。
关键词
光谱重建
训练样本
聚类算法
改进K均值聚类
Spectral reflectance reconstruction
Training samples
Clustering algorithm
Improved K-means clustering
