  A map-guided superpixel segmentation method for hyperspectral imagery is
developed and introduced. The proposed approach develops a
hyperspectral-appropriate version of the SLIC superpixel segmentation
algorithm, leverages map information to guide segmentation, and incorporates
the semi-supervised Partial Membership Latent Dirichlet Allocation (sPM-LDA) to
obtain a final superpixel segmentation. The proposed method is applied to two
real hyperspectral data sets and quantitative cluster validity metrics indicate
that the proposed approach outperforms existing hyperspectral superpixel
segmentation methods.
