Superpixel-Guided Local Sparsity Prior for Hyperspectral Sparse Regression Unmixing

Sparse regression relaxes the difficulties of blind unmixing of hyperspectral data thanks to the spectral library. Many investigations, however, attach importance to global priors such as sparsity and low rankness. This letter proposes a local-global-based sparse regression unmixing method (LGSU), by introducing a local sparsity regularization to help boost the unmixing performance that only considers global sparsity. The proposed LGSU first uses a superpixel-based technique to yield a set of homogeneous superpixels for guiding local sparse regularization purposes. LGSU then considers a traditional
$\ell _{1}$
regularization to enhance global sparsity. Coupling with local and global sparsity constraints, the proposed LGSU can effectively estimate the abundance of a given image via the alternating direction method of multipliers. Experimental results obtained from synthetic and real hyperspectral images demonstrate the effectiveness of the proposed algorithm.