SPICE: a sparsity promoting iterated constrained endmember extraction algorithm with applications to landmine detection from hyperspectral imagery

Alina Zare; Paul Gader

doi:10.1117/12.722595

26 April 2007 SPICE: a sparsity promoting iterated constrained endmember extraction algorithm with applications to landmine detection from hyperspectral imagery

Alina Zare, Paul Gader

Author Affiliations +

Proceedings Volume 6553, Detection and Remediation Technologies for Mines and Minelike Targets XII; 655319 (2007) https://doi.org/10.1117/12.722595
Event: Defense and Security Symposium, 2007, Orlando, Florida, United States

Abstract

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.

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Alina Zare and Paul Gader "SPICE: a sparsity promoting iterated constrained endmember extraction algorithm with applications to landmine detection from hyperspectral imagery", Proc. SPIE 6553, Detection and Remediation Technologies for Mines and Minelike Targets XII, 655319 (26 April 2007); https://doi.org/10.1117/12.722595

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