Fuzzy C-Multiple-Means Clustering for Hyperspectral Image
Xiaojun Yang, Mingjun Zhu, Bo Sun, Zheng Wang, Feiping Nie
Abstract
Currently, unsupervised hyperspectral image (HSI) segmentation methods are mainly implemented by clustering. Nevertheless, hyperspectral data contains a large amount of noise during the acquisition process, resulting in an abnormal distribution of many pixel points. Traditional clustering algorithms suffer from inaccurate segmentation when dealing with these data. For example, FCM is sensitive to anomalies in the clustering problem of HSI, that makes the clustering accuracy degraded. To address these problems, this paper proposes a method called Fuzzy C-Multiple-Means (FCMM). The method divides data points with multiple subclusters into defined <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</i> clusters. Different from the bottom-up coalescent strategy, the proposed FCMM transforms the problem of merging multiple subclusters into an optimisation problem for the fuzzy affiliation matrix, and updates the partitioning of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</i> subclusters and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</i> classes by an alternating iterative update method. This enhances the robustness of the algorithm and reduces the effect of outliers in the HSI datasets on the FCMM, which provides superior clustering results. Experiments on several HSI datasets validate the effectiveness of FCMM.