  This paper aims at developing a clustering approach with spectral images
directly from the compressive measurements of coded aperture snapshot spectral
imager (CASSI). Assuming that compressed measurements often lie approximately
in low dimensional subspaces corresponding to multiple classes, state of the
art methods generally obtains optimal solution for each step separately but
cannot guarantee that it will achieve the globally optimal clustering results.
In this paper, a low-rank subspace representation (LRSR) algorithm is proposed
to perform clustering on the compressed measurements. In addition, a subspace
structured norm is added into the objective of low-rank representation problem
exploiting the fact that each point in a union of subspaces can be expressed as
a sparse linear combination of all other points and that the matrix of the
points within each subspace is low rank. Simulation with real dataset
illustrates the accuracy of the proposed spectral image clustering approach.
