林业工程学报2025，Vol.10Issue(2)：130-137,8.DOI:10.13360/j.issn.2096-1359.202311003
一种基于核数据变换方法的遥感图像谱聚类算法
A spectral clustering algorithm for remote sensing images based on kernel data transformation method
摘要
Abstract
With the widespread application of remote sensing images across various industries,the processing of image has become increasingly important.To enable the application of spectral clustering algorithm to remote sensing image processing in the forestry engineering,this study proposes a new spectral clustering algorithm based on kernel data transformation and angular distance measurement.First,a new optimal feature extraction and unsupervised dimensionality reduction method,called the best kernel entropy component analysis(BKECA)method,is proposed.This method is developed by analyzing the general kernel entropy component analysis approach,which is based on multivariable kernel feature extraction.It incorporates concepts from information theory and Rayleigh quadratic entropy,which is closely associated with kernel density estimation.It uses an additional rotation based on the data structure in terms of class or cluster information to maximize the independence between components.The components optimally capture the high information potential parts of the data and the basis that maximizes this information potential concerning the number of retained components is directly determined.This approach ensures that the obtained solution retains as much or more information potential compared to what is achieved by the standard Kernel Entropy Component Analysis.A gradient-ascent approach is also proposed to solve the best kernel entropy component analysis optimization problem.The concrete implementation is that a simple early termination scheme is used to ensure that the gradient reaches a region where additional iterations do not significantly modify the cost function.In the second place,through the analysis of best kernel entropy component analysis transform and out of sample extension,a spectral clustering algorithm based on angular distance measurement is constructed,it adopts the kernel k-means clustering target of angular distance measure instead of the Euclidean distance measure.The angular distance in the best kernel entropy component analysis space is utilized during the optimization process to ensure convergence to a local optimum,thereby facilitating effective image clustering.Experimental results using multispectral satellite images demonstrate that the spectral clustering algorithm proposed in this study is not only effective for cloud screening in remote sensing images,but also has outperforms other advanced clustering algorithms in terms of classification performance.关键词
遥感图像/非线性特征提取/概率密度函数/k-均值/瑞利熵/谱聚类Key words
remote sensing image/nonlinear feature extraction/probability density function/k-means/Renyi entropy/spectral clustering分类
信息技术与安全科学引用本文复制引用
赵海军,陈华月,崔梦天..一种基于核数据变换方法的遥感图像谱聚类算法[J].林业工程学报,2025,10(2):130-137,8.基金项目
四川省自然科学基金(2022NSFSC0536) （2022NSFSC0536）
国家自然科学基金(12050410248). （12050410248）
