Spatial Sampling and Grouping Information Entropy Strategy Based on Kernel Fuzzy C-Means Clustering Method for Hyperspectral Band Selection

作     者：Zhang, Zhou Wang, Degang Sun, Xu Zhuang, Lina Liu, Rong Ni, Li 

作者机构：East China Univ Technol Fac Geomat Nanchang 330013 Jiangxi Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Key Lab Computat Opt Imaging Technol Beijing 100094 Peoples R China Univ Chinese Acad Sci Coll Resources & Environm Beijing 100049 Peoples R China 

出 版 物：《REMOTE SENSING》 (遥感)

年 卷 期：2022年第14卷第19期

页      面：5058页

核心收录：

学科分类：0830[工学-环境科学与工程（可授工学、理学、农学学位）] 1002[医学-临床医学] 070801[理学-固体地球物理学] 07[理学] 08[工学] 0708[理学-地球物理学] 0816[工学-测绘科学与技术] 

基　　金：National Natural Science Foundation of China 

主　　题：hyperspectral image band selection kernel fuzzy c-means clustering spatial sampling strategy information entropy 

摘      要：The high spectral resolution of hyperspectral images (HSIs) provides rich information but causes data redundancy, which imposes a computational burden on practical applications. Band selection methods can select a subset of HSI without changing the main information to reduce the spectral dimension. Clustering-based methods can reduce band correlation significantly, but traditional clustering methods are mostly hard clustering and are not accurate enough to partition the bands. An unsupervised band selection method based on fuzzy c-means clustering (FCM) was introduced to tackle this problem. However, FCM can easily obtain the local optimal solution and take a long time to process high-dimensional data. Hence, this work applies kernel function and a sampling strategy to reduce calculation time, and information entropy is used to initialize the cluster center. A kernel FCM algorithm based on spatial sampling and a grouping information entropy strategy is proposed and called SSGIE-KFCM. This method not only optimizes the calculation process and reduces the amount of computation data, accelerating the calculation efficiency, but also adopts grouping information entropy to improve the probability of obtaining a global optimal solution. Classification experiments on two public HSI datasets show that: (1) The classification performance of the whole band can be achieved or even exceeded by using only a small number of bands to achieve the purpose of dimensionality reduction. (2) The classification accuracy can be improved compared with the FCM method. (3) With the introduction of sampling strategy and kernel function, the computational speed is at least 24 times faster than that of FCM. It has been proven that the SSGIE-KFCM method can significantly reduce the amount of HSI while retaining the primary information of the original data, which further promotes the research and application of HSI in the remote sensing area.