Comparing Quadtree Region Partitioning Metrics for Hyperspectral Unmixing
An approach for unsupervised unmixing using quadtree region partitioning is studied. Images are partitioned in spectrally homogeneous regions using quadtree region partitioning. Unmixing is performed in each individual region using the positive matrix factorization and extracted endmembers are the clustered in endmembers classes which account for the variability of spectral endmembers across the scene. The proposed method lends itself to an unsupervised approach. In the paper, the effect of different spectral variability metrics in the splitting of the image using quadtree partitioning is studied. Experimental results using the AVIRIS AP Hill image show that the Shannon entropy produces the image partitioning that agrees with published ground truth.