Open AccessFast robust fuzzy clustering based on bipartite graph for hyper‐spectral image classification
Author(s) -
Liu Han,
Wu Chengmao,
Li Changxing,
Zuo Yanqun
Publication year - 2022
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/ipr2.12581
Subject(s) - cluster analysis , fuzzy clustering , spectral clustering , correlation clustering , pattern recognition (psychology) , cure data clustering algorithm , mathematics , canopy clustering algorithm , artificial intelligence , flame clustering , bipartite graph , data stream clustering , computer science , data mining , algorithm , graph , discrete mathematics
Abstract Hyper‐spectral image (HSI) clustering has become a hot spot in remote sensing image research. However, due to the large amount and high dimension of HSI data, some traditional image clustering methods are no longer suitable, and even lead to poor clustering performance and long computing time. Therefore, this paper proposes a novel clustering method for HSI to make up for the shortcomings of traditional clustering methods. Firstly, a fuzzy similarity matrix is constructed by using the bipartite graph to obtain low‐dimensional hyper‐spectral data, which can reduce the complexity and the operation time of the algorithm. Secondly, fuzzy membership mapping is carried out through the constructed bipartite graph, and a non‐negative regularization term is added to limit ill‐conditioned problems. Thirdly, this paper uses the Geman‐McClure function to optimize Euclidean distance. Finally, in this paper, the spectral information of HSI is considered while the neighbourhood information of HSI is added to achieve the unity of space spectrum, which enhances the robustness of the algorithm and improves the clustering performance of the algorithm. Compared with existing HSI clustering algorithms, the proposed algorithm has higher clustering accuracy and efficiency.