Structured Sparse Method for Hyperspectral Unmixing
This work addresses the limitation of ignoring spatial information in hyperspectral unmixing, which is important for remote sensing and image analysis applications, but it is incremental as it builds on existing NMF methods.
The paper tackles the problem of hyperspectral unmixing by proposing a Structured Sparse regularized Nonnegative Matrix Factorization method that incorporates spatial information and sparse representations, resulting in significant outperformance over state-of-the-art methods on real datasets with varying noise levels.
Hyperspectral Unmixing (HU) has received increasing attention in the past decades due to its ability of unveiling information latent in hyperspectral data. Unfortunately, most existing methods fail to take advantage of the spatial information in data. To overcome this limitation, we propose a Structured Sparse regularized Nonnegative Matrix Factorization (SS-NMF) method from the following two aspects. First, we incorporate a graph Laplacian to encode the manifold structures embedded in the hyperspectral data space. In this way, the highly similar neighboring pixels can be grouped together. Second, the lasso penalty is employed in SS-NMF for the fact that pixels in the same manifold structure are sparsely mixed by a common set of relevant bases. These two factors act as a new structured sparse constraint. With this constraint, our method can learn a compact space, where highly similar pixels are grouped to share correlated sparse representations. Experiments on real hyperspectral data sets with different noise levels demonstrate that our method outperforms the state-of-the-art methods significantly.