  The dictionary-aided sparse regression (SR) approach has recently emerged as
a promising alternative to hyperspectral unmixing (HU) in remote sensing. By
using an available spectral library as a dictionary, the SR approach identifies
the underlying materials in a given hyperspectral image by selecting a small
subset of spectral samples in the dictionary to represent the whole image. A
drawback with the current SR developments is that an actual spectral signature
in the scene is often assumed to have zero mismatch with its corresponding
dictionary sample, and such an assumption is considered too ideal in practice.
In this paper, we tackle the spectral signature mismatch problem by proposing a
dictionary-adjusted nonconvex sparsity-encouraging regression (DANSER)
framework. The main idea is to incorporate dictionary correcting variables in
an SR formulation. A simple and low per-iteration complexity algorithm is
tailor-designed for practical realization of DANSER. Using the same dictionary
correcting idea, we also propose a robust subspace solution for dictionary
pruning. Extensive simulations and real-data experiments show that the proposed
method is effective in mitigating the undesirable spectral signature mismatch
effects.
