Adaptive Multi-Order Graph Regularized NMF with Dual Sparsity for Hyperspectral Unmixing
This is an incremental improvement for remote sensing applications, addressing limitations in graph learning and parameter tuning in NMF-based hyperspectral unmixing.
The paper tackled the problem of hyperspectral unmixing by proposing an adaptive multi-order graph regularized NMF method with dual sparsity, which improved unmixing results on simulated and real data compared to existing methods.
Hyperspectral unmixing (HU) is a critical yet challenging task in remote sensing. However, existing nonnegative matrix factorization (NMF) methods with graph learning mostly focus on first-order or second-order nearest neighbor relationships and usually require manual parameter tuning, which fails to characterize intrinsic data structures. To address the above issues, we propose a novel adaptive multi-order graph regularized NMF method (MOGNMF) with three key features. First, multi-order graph regularization is introduced into the NMF framework to exploit global and local information comprehensively. Second, these parameters associated with the multi-order graph are learned adaptively through a data-driven approach. Third, dual sparsity is embedded to obtain better robustness, i.e., $\ell_{1/2}$-norm on the abundance matrix and $\ell_{2,1}$-norm on the noise matrix. To solve the proposed model, we develop an alternating minimization algorithm whose subproblems have explicit solutions, thus ensuring effectiveness. Experiments on simulated and real hyperspectral data indicate that the proposed method delivers better unmixing results.