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

Accurate unsupervised classification of hyperspectral images (HSIs) is challenging and has drawn widespread attention in remote sensing due to its inherent complexity. Although significant efforts have been made to develop 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 full HSIs. Furthermore, since SSC ignores the spatial information in the HSIs, its discrimination capability is limited, hampering the clustering results' spatial homogeneity. To address these two relevant issues, in this article, 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. Extensive simulations with our proposed method demonstrate its effectiveness in unsupervised HSI classification, 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 x 10(4) spectral pixels.

Automated summary

This article proposes 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. Extensive simulations demonstrate the effectiveness of this method in unsupervised HSI classification.