Hyperspectral image feature selection for the fuzzy c-means spatial and spectral clustering

Hyperspectral image clustering is commonly applied for unsupervised classification. However, the clustering results of traditional methods are not sufficient seeing the nature of the image as a data cube with high dimensionality. In addition, the complex relations between spatial neighboring pixels are not considered in traditional methods. In this paper the fuzzy c-means clustering is revisited and customized. The proposed approach aims at the reduction of dimensionality of the data cube while preserving the most relevant spectral features and the improvement of the clustering result. The integration of spatial feature can express natural dependence between neighboring pixels and enhance the clustering. For that the presented approach starts by a band selection method based on the hierarchical clustering of spectral bands using the mutual information measure to reduce the dimensionality of the image. Then, a new version of the fuzzy c-means clustering algorithm is proposed; this version includes spatial and spectral features. Experimental result on real hyperspectral data shows an improvement on the accuracy over conventional clustering methods.