SPICE: A Sparsity Promoting Iterated Constrained Endmember Extraction Algorithm with Applications to Landmine Detection from Hyperspectral Imagery

摘要

An extension of the Iterated Constrained Endmembers (ICE) algorithm that incorporates sparsity promoting priors to find the correct number of endmembers is presented. In addition to solving for endmembers and endmember fractional maps, this algorithm attempts to autonomously determine the number of endmembers required for a particular scene. The number of endmembers is found by adding a sparsity-promoting term to ICE's objective function. This method is applied to long wave infrared, LWIR, hyperspectral data to seek out vegetation endmembers and define a vegetation mask for the reduction of false alarms in landmine data.