Two effective and computationally efficient pure-pixel based algorithms for hyperspectral endmember extraction

Endmember extraction is of prime importance in the process of hyperspectral unmixing so as to study the mineral composition of a landscape from its hyperspectral observations. Though, a whole bunch of pure-pixel based endmember extraction algorithms exists, the quest for a reliable, repeatable, and computationally efficient endmember extraction algorithm still prevails. In this work, we propose two pure-pixel based endmember extraction algorithms called simplex estimation by projection (SIMPLE-Pro) algorithm and p-norm based pure pixel identification (TRI-P) algorithm. The end member identifiability of the proposed two algorithms is theoretically proved under the pure pixel assumption. Both algorithms never require any initializations and hence they are repeatable. Monte Carlo simulations are performed to demonstrate the superior efficacy and computational efficiency of the proposed two algorithms over some existing benchmark endmember extraction algorithms.