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

Abstract

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.