Advertisement

Spectral Unmixing Technique of HSI

  • Liguo WangEmail author
  • Chunhui Zhao
Chapter
  • 2.3k Downloads

Abstract

Relative to the classification technique, the spectral unmixing (Keshava and Mustard in IEEE Trans Sig Process Mag 19:44–57, 2002) i.e., soft classification technique started late. Although the spectral resolution of the hyperspectral image has been improved greatly, the spatial resolution of the corresponding land object target of the pixel has been relatively low.

Keywords

Training Sample Hyperspectral Image Mixed Pixel Hard Classification Pure Pixel 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Asner GP, Lobell DB (2000) A biogeophysical approach for automated SWIR unmixing of soils and vegetation. Remote Sens Environ 74:99–112CrossRefGoogle Scholar
  2. Bateson CA, Asner GP, Wessman CA (2000) Endmember bundles: a new approach to incorporating endmember variability into spectral mixture analysis. IEEE Trans Geosci Remote Sens 38:1083–1094CrossRefGoogle Scholar
  3. Brown M, Lewis H, Gunn S (2000) Linear spectral mixture models and support vector machine for remote sensing. IEEE Trans Geosci Remote Sens 38:2346–2360CrossRefGoogle Scholar
  4. Du Q, Chein-I Chang (2004) Linear mixture analysis-based compression for hyperspectal image analysis. IEEE Trans Geosci Remote Sens 42(4):875–891CrossRefGoogle Scholar
  5. Emami H (2005) Introducing correctness coefficient as an accuracy measure for sub pixel classification results. http://www.ncc.org.ir/articles/poster83/H.Emami.pdf
  6. Fletcher R (1987) Practical Methods of Optimization. Chichester. Wiley, UKGoogle Scholar
  7. Junwu L, Roger LK, Nicolas Y (2002) An unmixing algorithm based on vicinal information. Geosci Remote Sens Symp 3:1453–1455Google Scholar
  8. Keshava N, Mustard JF (2002) Spectral unmixing. IEEE Trans Sig Process Mag 19(1):44–57CrossRefGoogle Scholar
  9. Qing H, Zhen X (1999) Neighbor field-based mixed pixel interpretation. J Northern Jiaotong Univ 23(4):118–121Google Scholar
  10. Strahler AH, Boschetti L, Foody GM et al (2006) Global land cover validation: recommendations for evaluation and accuracy assessment of global land cover maps. http://nofc.cfs.nrcan.gc.ca/gofc-gold/Report%20Series/GOLD_25.pdf
  11. Wang LG, Jia XP (2009) Integration of soft and hard classification using extended support vector machine. IEEE Trans Geosci Remote Sens Lett 6(3):544–547Google Scholar
  12. Winter ME, Lucey PG, Steuter D (2003) Examining hyperspectral unmixing error reduction due to stepwise unmixing. Proc SPIE-Int Soc Opt Eng 5093:380–389Google Scholar

Copyright information

© National Defense Industry Press, Beijing and Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Harbin Engineering UniversityHarbinChina

Personalised recommendations