Spectral–Spatial Hyperspectral Image Classification via Non-local Means Filtering Feature Extraction

Abstract

Hyperspectral image (HSI) classification has been a hot topic of research in recent years. The integration of spectral and spatial context is an effective method for HSI classification. This paper proposes a classification method of HSI based on non-local means (NLM) filtering. Firstly, the classification result of HSI is obtained by adopting the support vector machines. Then, the optimization probability image of spatial structure is obtained by using the spatial context information in the first principal component or the first three principal components of HSI to optimize the initial probability map through the NLM filtering. Finally, the final classification results are calculated based on the maximum probability. Experiment results on three real hyperspectral data demonstrate that the proposed NLM filtering based classification method can improve the classification accuracy significantly. Classification results show the effectiveness and superiority of the proposed methods when compared with other methods.