  Hyperspectral image (HSI) clustering, which aims at dividing hyperspectral
pixels into clusters, has drawn significant attention in practical
applications. Recently, many graph-based clustering methods, which construct an
adjacent graph to model the data relationship, have shown dominant performance.
However, the high dimensionality of HSI data makes it hard to construct the
pairwise adjacent graph. Besides, abundant spatial structures are often
overlooked during the clustering procedure. In order to better handle the high
dimensionality problem and preserve the spatial structures, this paper proposes
a novel unsupervised approach called spatial-spectral clustering with anchor
graph (SSCAG) for HSI data clustering. The SSCAG has the following
contributions: 1) the anchor graph-based strategy is used to construct a
tractable large graph for HSI data, which effectively exploits all data points
and reduces the computational complexity; 2) a new similarity metric is
presented to embed the spatial-spectral information into the combined adjacent
graph, which can mine the intrinsic property structure of HSI data; 3) an
effective neighbors assignment strategy is adopted in the optimization, which
performs the singular value decomposition (SVD) on the adjacent graph to get
solutions efficiently. Extensive experiments on three public HSI datasets show
that the proposed SSCAG is competitive against the state-of-the-art approaches.
