Hyperspectral image clustering

A Fast and Accurate Similarity-constrained Subspace Clustering Algorithm for Hyperspectral Image

In this paper, we propose a fast algorithm that obtains a sparse representation coefficient matrix by first selecting a small set of pixels that best represent their neighborhood. Then, it performs spatial filtering to enforce the connectivity of neighboring pixels and uses fast spectral clustering to get the final clustering map.