摘要
大规模高光谱图像聚类算法广泛应用于遥感领域,主要包括K均值(K-means)聚类、谱聚类算法等。然而谱聚类算法仍然有局限性,由于其计算复杂性高所以不适用于大规模问题。基于锚图的谱聚类算法在一定程度上能够减少计算的成本,然而在处理大规模高光谱图像数据时,锚点需要足够密集,否则无法获得合理的精度,这使得该聚类算法的计算成本急剧增加。为了克服这些问题,提出了一种新的基于多层二部图的高光谱快速谱聚类算法。该算法首先使用二叉树选点方式选取锚点,然后选择多层锚点构建多层锚点图,接着构造一个多层二部图,最后对该图进行谱分析。实验证明了提出算法的高效性。
Large-scale hyperspectral image clustering algorithms are widely used in the field of remote sensing, including K-means clustering and spectral clustering algorithms. However, the spectral clustering algorithm still has its limitations. Because of its high computational complexity, it is not suitable for largescale problems. The spectral clustering algorithm based on the anchor graph can reduce the computational cost to a certain extent. However, in the large-scale hyperspectral image data processsing, the anchor points need to be dense enough, otherwise reasonable accuracy cannot be obtained. This makes the computing cost of the clustering algorithm increase sharply. In order to overcome these problems, a new fast spectral clustering algorithm based on multi-layer bipartite graph is proposed. Firstly, the anchor points are selected by the binary tree, and the multi-layer anchor points are selected to construct the multi-layer anchor point graph. Then a multi-layer bipartite graph is constructed, and finally the spectrum of the graph is analyzed. The high efficiency of the proposed algorithm is proved by experiments.
作者
李思远
郑致远
杜晓颜
刘彤
杨晓君
Li Siyuan;Zheng Zhiyuan;Du Xiaoyan;Liu Tong;Yang Xiaojun(College of Information Engineering,Guangdong University of Technology,Guangzhou 510006,Guangdong,China;Rocket Force University of Engineering,Chinese People’s Liberation Army,Xi’an 710025,Shaanxi,China;Chinese People’s Liberation Army 96962 Troops,Beijing 102206,China)
出处
《激光与光电子学进展》
CSCD
北大核心
2022年第12期121-127,共7页
Laser & Optoelectronics Progress
基金
国家重点研发计划(2018YFB1802100)
广东省重点领域研发计划(2018B010115001)
广东省自然科学基金(2021A1515011141)
广东省珠江人才计划本土创新科研团队资助项目(2017BT01X168)。
关键词
图像处理
高光谱图像
多层图
二叉树
二部图
谱聚类
image processing
hyperspectral image
multi-layer graph
binary tree
bipartite graph
spectral clustering
