1 research outputs found
Endmember Extraction on the Grassmannian
Endmember extraction plays a prominent role in a variety of data analysis
problems as endmembers often correspond to data representing the purest or best
representative of some feature. Identifying endmembers then can be useful for
further identification and classification tasks. In settings with
high-dimensional data, such as hyperspectral imagery, it can be useful to
consider endmembers that are subspaces as they are capable of capturing a wider
range of variations of a signature. The endmember extraction problem in this
setting thus translates to finding the vertices of the convex hull of a set of
points on a Grassmannian. In the presence of noise, it can be less clear
whether a point should be considered a vertex. In this paper, we propose an
algorithm to extract endmembers on a Grassmannian, identify subspaces of
interest that lie near the boundary of a convex hull, and demonstrate the use
of the algorithm on a synthetic example and on the 220 spectral band AVIRIS
Indian Pines hyperspectral image.Comment: To appear in Proceedings of the 2018 IEEE Data Science Workshop,
Lausanne, Switzerlan