  This paper presents a sparse representation-based classification approach
with a novel dictionary construction procedure. By using the constructed
dictionary sophisticated prior knowledge about the spatial nature of the image
can be integrated. The approach is based on the assumption that each image
patch can be factorized into characteristic spatial patterns, also called
shapelets, and patch-specific spectral information. A set of shapelets is
learned in an unsupervised way and spectral information are embodied by
training samples. A combination of shapelets and spectral information are
represented in an undercomplete spatial-spectral dictionary for each individual
patch, where the elements of the dictionary are linearly combined to a sparse
representation of the patch. The patch-based classification is obtained by
means of the representation error. Experiments are conducted on three
well-known hyperspectral image datasets. They illustrate that our proposed
approach shows superior results in comparison to sparse representation-based
classifiers that use only limited spatial information and behaves competitively
with or better than state-of-the-art classifiers utilizing spatial information
and kernelized sparse representation-based classifiers.
