Robust Endmember detection using L1 norm factorization

The results from L₁-Endmembers display the algorithm´s stability and accuracy with increasing levels of noise. The algorithm was extremely stable in the number of endmembers when compared to the SPICE algorithm and the Virtual Dimensionality methods for estimating the number of endmembers. Furthermore, the results shown for this algorithm were generated with the same parameter set for all of the data sets, from two-dimensional data to 51-dimensional real hyperspectral data. This indicates L₁-Endmembers may lack of sensitivity to parameter value settings. The L₁-Endmembers algorithm requires several quadratic programming steps per iteration. These can be completed directly in quadratic programming software packages such as CPLEX and take advantage of any running time reductions the software packages provide. Investigations will be conducted into whether the specific form of this algorithm, particularly with respect to the constraints on the abundance values, can be used to reduce the running time.