Hyperspectral Band Reduction Based on Rough Sets and Fuzzy C-Means Clustering
基于粗糙集和模糊聚类的超谱波段约简

A method of hyperspectral band reduction based on Rough Sets (RS) and Fuzzy C-Means (FCM) clustering is proposed, which consists of the following two steps. First, Fuzzy C-Means clustering algorithm is used to classify the original bands into equivalent band groups, which employs the concept of attribute dependency defined in RS to define the distance between a group and the cluster center, viz. the correlatives of adjacent bands. Then the data is reduced by selecting the only one from each group with maximum grade of fuzzy membership. With this approach, great dimension of band is decreased while preserving much wanted information. Simulation results prove the effectiveness of this approach.