Hyperspectral Unmixing Based on Clustered Multitask Networks
This work addresses spectral unmixing in hyperspectral remote sensing, an incremental improvement for data processing in this domain.
The paper tackles hyperspectral unmixing by clustering images with fuzzy c-means and applying a sparsity-constrained distributed optimization algorithm using a network of clusters, optimized with diffusion LMS, which shows advantages over other methods in simulation results.
Hyperspectral remote sensing is a prominent research topic in data processing. Most of the spectral unmixing algorithms are developed by adopting the linear mixing models. Nonnegative matrix factorization (NMF) and its developments are used widely for estimation of signatures and fractional abundances in the SU problem. Sparsity constraints was added to NMF, and was regularized by $ L_ {q} $ norm. In this paper, at first hyperspectral images are clustered by fuzzy c- means method, and then a new algorithm based on sparsity constrained distributed optimization is used for spectral unmixing. In the proposed algorithm, a network including clusters is employed. Each pixel in the hyperspectral images considered as a node in this network. The proposed algorithm is optimized with diffusion LMS strategy, and then the update equations for fractional abundance and signature matrices are obtained. Simulation results based on defined performance metrics illustrate advantage of the proposed algorithm in spectral unmixing of hyperspectral data compared with other methods.