  Graph-based semi-supervised learning methods, which deal well with the
situation of limited labeled data, have shown dominant performance in practical
applications. However, the high dimensionality of hyperspectral images (HSI)
makes it hard to construct the pairwise adjacent graph. Besides, the fine
spatial features that help improve the discriminability of the model are often
overlooked. To handle the problems, this paper proposes a novel
spatial-spectral HSI classification method via multiple random anchor graphs
ensemble learning (RAGE). Firstly, the local binary pattern is adopted to
extract the more descriptive features on each selected band, which preserves
local structures and subtle changes of a region. Secondly, the adaptive
neighbors assignment is introduced in the construction of anchor graph, to
reduce the computational complexity. Finally, an ensemble model is built by
utilizing multiple anchor graphs, such that the diversity of HSI is learned.
Extensive experiments show that RAGE is competitive against the
state-of-the-art approaches.
