Dynamic topic discovery through sequential projections

We consider a novel problem of endmember detection in hyperspectral imagery where signal of frequency bands are probed sequentially. We propose an adaptive strategy in controlling the sensing order to maximize the normalized solid angle as a robustness measure of the problem geometry. This is based on efficiently identifying pure pixels that are unique to each endmember and exploiting information from a spectral library known in advance though sequential random projections. We present simulations on synthetic datasets to demonstrate the merits of our scheme in reducing the observation cost.