  Dimensionality reduction is a crucial preprocessing for hyperspectral data
analysis - finding an appropriate subspace is often required for subsequent
image classification. In recent work, we proposed supervised angular
information based dimensionality reduction methods to find effective subspaces.
Since unlabeled data are often more readily available compared to labeled data,
we propose an unsupervised projection that finds a lower dimensional subspace
where local angular information is preserved. To exploit spatial information
from the hyperspectral images, we further extend our unsupervised projection to
incorporate spatial contextual information around each pixel in the image.
Additionally, we also propose a sparse representation based classifier which is
optimized to exploit spatial information during classification - we hence
assert that our proposed projection is particularly suitable for classifiers
where local similarity and spatial context are both important. Experimental
results with two real-world hyperspectral datasets demonstrate that our
proposed methods provide a robust classification performance.
