Hyperspectral Image Unmixing via Alternating Projected Subgradients
A. Zymnis, S.-J. Kim, J. Skaf, M. Parente, and S. Boyd
Proceedings Asilomar Conference on Signals, Systems, and Computers, pages 1164–1168, November 2007.
We formulate the problem of hyperspectral image unmixing as a nonconvex optimization problem, similar to nonnegative matrix factorization. We present a heuristic for approximately solving this problem using an alternating projected subgradient approach. Finally, we present the result of applying this method on the 1990 AVIRIS image of Cuprite, Nevada and show that our results are in agreement with similar studies on the same data.