Hyperspectral Data Unmixing Using GNMF Method and Sparseness Constraint
This is an incremental improvement for hyperspectral imaging researchers, addressing mixed pixel decomposition in low-resolution sensor data.
The paper tackled hyperspectral image unmixing by applying graph-regularized nonnegative matrix factorization with sparseness constraints, showing improved effectiveness over other methods as quantified by AAD and SAD measures on simulated AVIRIS Indian Pines data.
Hyperspectral images contain mixed pixels due to low spatial resolution of hyperspectral sensors. Mixed pixels are pixels containing more than one distinct material called endmembers. The presence percentages of endmembers in mixed pixels are called abundance fractions. Spectral unmixing problem refers to decomposing these pixels into a set of endmembers and abundance fractions. Due to nonnegativity constraint on abundance fractions, nonnegative matrix factorization methods (NMF) have been widely used for solving spectral unmixing problem. In this paper we have used graph regularized (GNMF) method with sparseness constraint to unmix hyperspectral data. This method applied on simulated data using AVIRIS Indian Pines dataset and USGS library and results are quantified based on AAD and SAD measures. Results in comparison with other methods show that the proposed method can unmix data more effectively.