Endmember Detection using Enhanced Constrained Optimization in Hyperspectral Imaging

YÜKSEL ERDEM S. E.

22nd IEEE Signal Processing and Communications Applications Conference (SIU), Trabzon, Turkey, 23 - 25 April 2014, pp.1023-1026, (Full Text) identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume:
  • Doi Number: 10.1109/siu.2014.6830406
  • City: Trabzon
  • Country: Turkey
  • Page Numbers: pp.1023-1026
  • Hacettepe University Affiliated: Yes

Abstract

A new algorithm is presented for linear spectral mixture analysis that respects the constraints on the endmembers. The results show that it provides a more robust solution as compared to the ICE and SPICE algorithms due to the use of constrained quadratic optimization for endmember detection.