  Accurate land cover segmentation of spectral images is challenging and has
drawn widespread attention in remote sensing due to its inherent complexity.
Although significant efforts have been made for developing a variety of
methods, most of them rely on supervised strategies. Subspace clustering
methods, such as Sparse Subspace Clustering (SSC), have become a popular tool
for unsupervised learning due to their high performance. However, the
computational complexity of SSC methods prevents their use on large spectral
remotely sensed datasets. Furthermore, since SSC ignores the spatial
information in the spectral images, its discrimination capability is limited,
hampering the clustering results' spatial homogeneity. To address these two
relevant issues, 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 segmentation. Extensive simulations with
our method demonstrate its effectiveness in land cover segmentation, obtaining
remarkable high clustering performance compared with state-of-the-art SSC-based
algorithms and even novel unsupervised-deep-learning-based methods. Besides,
the proposed method is up to three orders of magnitude faster than SSC when
clustering more than $2 \times 10^4$ spectral pixels.
