  Pixel-wise classification, where each pixel is assigned to a predefined
class, is one of the most important procedures in hyperspectral image (HSI)
analysis. By representing a test pixel as a linear combination of a small
subset of labeled pixels, a sparse representation classifier (SRC) gives rather
plausible results compared with that of traditional classifiers such as the
support vector machine (SVM). Recently, by incorporating additional structured
sparsity priors, the second generation SRCs have appeared in the literature and
are reported to further improve the performance of HSI. These priors are based
on exploiting the spatial dependencies between the neighboring pixels, the
inherent structure of the dictionary, or both. In this paper, we review and
compare several structured priors for sparse-representation-based HSI
classification. We also propose a new structured prior called the low rank
group prior, which can be considered as a modification of the low rank prior.
Furthermore, we will investigate how different structured priors improve the
result for the HSI classification.
