  Dictionary pruning methods perform unmixing by identifying a smaller subset
of active spectral library elements that can represent the image efficiently as
a linear combination. This paper presents a new nuclear norm difference based
approach for dictionary pruning utilizing the low rank property of
hyperspectral data. The proposed workflow calculates the nuclear norm of
abundance of the original data assuming the whole spectral library as
endmembers. In the next step, the algorithm calculates nuclear norm of
abundance after appending a spectral library element with the data. The
spectral library elements having the maximum difference in the nuclear norm of
the obtained abundance matrices are suitable candidates for being image
endmember. The proposed workflow is verified with a large number of synthetic
data generated by varying condition as well as some real images.
