Fuzzy C-Multiple-Means Clustering for Hyperspectral Image

Currently, unsupervised hyperspectral image (HSI) segmentation methods are mainly implemented by clustering. Nevertheless, hyperspectral data contain 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, which makes the clustering accuracy degraded. To address these problems, this letter proposes a method called fuzzy C-multiple-means (FCMM). The method divides data points with multiple subclusters into defined
$c$
clusters. Different from the bottom-up coalescent strategy, the proposed FCMM transforms the problem of merging multiple subclusters into an optimization problem for the fuzzy affiliation matrix and updates the partitioning of the
$q$
subclusters and
$c$
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.