Loss of target information in full pixel and subpixel target detection in hyperspectral data with and without dimensionality reduction

  • Amrita BhandariEmail author
  • K. C. Tiwari
Original Paper


In most hyperspectral target detection applications, targets are usually small and require both spatial as well as spectral detection. Hyperspectral imaging facilitates target detection (TD) applications greatly, however, due to large spectral content, hyperspectral data requires dimensionality reduction (DR) which also leads to loss of target information both at full pixel and subpixel level. Literature reports many DR and TD algorithms in practice. Several studies have focussed on assessing the loss of target information in DR, however, not much work seems to have been done to assess loss of target information in full pixel and subpixel TD in hyperspectral data with and without DR. This paper seeks to study various combinations of DR techniques combined with full pixel and subpixel TD algorithms. The results indicate that in the case of full pixel targets, both DR and TD contribute to the loss of target information, however, there is more loss of target information in the case when DR precedes TD in comparison to a case where TD is applied without DR. In the case of subpixel TD, however, there appears to be loss of subpixel target information in the case where TD alone is performed in comparison to a case where DR precedes TD.


Dimensionality reduction Full pixel target detection Subpixel target detection Spectral unmixing Mixed pixel Target information 



  1. Agarwal A, El-Ghazawi T, El-Askary H, Le-Moigne J (2007) Efficient hierarchical-PCA dimension reduction for hyperspectral imagery. In: IEEE international symposium on signal processing and information technology (ISSPIT), Giza, 2008, pp 353–356.
  2. Altmann Y, Halimi A, Dobigeon N, Tourneret JY (2012) Supervised nonlinear spectral unmixing using a post-nonlinear mixing model for hyperspectral imagery. IEEE Trans Image Process 21(6):3017–3025MathSciNetCrossRefzbMATHGoogle Scholar
  3. Angelov PP, Gu X (2018) Deep rule-based classifier with human-level performance and characteristics. Inf Sci 463:196–213CrossRefGoogle Scholar
  4. Binol H, Ochilov S, Alam MA, Bal A (2016) Target oriented dimensionality reduction of hyperspectral data by Kernel–Fukunaga–Koontz transform. Elsevier Ltd, Amsterdam. Google Scholar
  5. Boardman JW (1998) Leveraging the high dimensionality of AVIRIS data for improved sub-pixel target unmixing and rejection of false positives: mixture tuned matched filtering. In: Proceedings of the 7th annual JPL airborne geoscience workshop, vol 97, no 1. JPL publication, p 55Google Scholar
  6. Boardman JW, Kruse FA, Green RO (1995) Mapping target signatures via partial unmixing of AVIRIS data. In: Summaries V JPL airborne earth science workshop, Pasadena, CA, 01, pp 23–26Google Scholar
  7. Borel C, Gerstl S (1994) Nonlinear spectral mixing models for vegetative and soil surfaces. Remote Sens Environ 47(3):403–416CrossRefGoogle Scholar
  8. Chang C-I (2003) Hyperspectral imaging: techniques for spectral detection and classification. Kluwer, NorwellCrossRefGoogle Scholar
  9. Chang C-I (2005) Orthogonal subspace projection (OSP) revisited: A comprehensive study and analysis. IEEE Trans Geosci Remote Sens 43(3):502–518MathSciNetCrossRefGoogle Scholar
  10. Chang C-I, Du Q (2004) Estimation of number of spectrally distinct signal sources in hyperspectral imagery. IEEE Trans Geosci Remote Sens 42(3):608–619CrossRefGoogle Scholar
  11. Chang CI, Liu JM, Chieu BC, Wang CM, Lo CS, Chung PC, Ren H, Yang CW, Ma DJ (2000) A generalized constrained energy minimization approach to subpixel target detection for multispectral imagery. Opt Eng 39(5):1275–1281CrossRefGoogle Scholar
  12. Chen JY, Reed SI (1987) A detection algorithm for optical targets in clutter. IEEE Trans Aerosp Electron Syst AES-23(1):46–59CrossRefGoogle Scholar
  13. Cocks T, Jenssen R, Stewart A, Wilson I, Shields T (1998) The HyMap airborne hyperspectral sensor: the system, calibration and performance. In: Proc. 1st EARSeL workshop on imaging spectroscopy, EARSeL, Paris, pp 37–43Google Scholar
  14. Davood A, Safari A (2012) Support vector machine for target detection in hyperspectral images. TS06I—remote sensing II, 6135, pp 1–10Google Scholar
  15. Du Q, Chang CI (2004) A signal-decomposed and interference-annihilated approach to hyperspectral target detection. IEEE Trans Geosci Remote Sens 42(4):892–906. CrossRefGoogle Scholar
  16. Eismann MT (2012) Hyperspectral remote sensing. SPIE Press, Bellingham. CrossRefGoogle Scholar
  17. Fan W, Hu B, Miller J, Li M (2009) Comparative study between a new nonlinear model and common linear model for analysing laboratory simulated-forest hyperspectral data. Int J Remote Sens 30(11):2951–2962CrossRefGoogle Scholar
  18. Farrand WH, Harsanyi JC (2004) Mapping the distribution of mine tailings in the Coeur d’Alene River Valley Idaho, through the use of a constrained energy minimization technique. Remote Sens Environ 59(1):64–76CrossRefGoogle Scholar
  19. Green AA, Berman M, Switzer P, Craig MD (1988) A transformation for ordering multispectral data in terms of image quality with implications for noise removal. IEEE Trans Geosci Remote Sens 26:65–74CrossRefGoogle Scholar
  20. Gu X, Angelov PP (2018a) Semi-supervised deep rule-based approach for image classification. Appl Soft Comput 68:53–68CrossRefGoogle Scholar
  21. Gu X, Angelov PP (2018b) A massively parallel deep rule-based ensemble classifier for remote sensing scenes. IEEE Geosci Remote Sens Lett 15(3):345–349CrossRefGoogle Scholar
  22. Guilfoyle KJ, Althouse ML, Chang CI (2001) A quantitative and comparative analysis of linear and nonlinear spectral mixture models using radial basis function neural networks. IEEE Trans Geosci Remote Sens 39:2314–2318CrossRefGoogle Scholar
  23. Harsanyi JC (1993) Detection and classification of subpixel spectral signatures in hyperspectral image sequences, Ph.D. dissertation, Dept. Elect. Eng., Univ. Maryland, Baltimore, MD, USAGoogle Scholar
  24. Harsanyi JC, Chang CI (1994) Hyperspectral image classification and dimensionality reduction: An orthogonal subspace projection approach. IEEE Trans Geosci Remote Sens 32(4):779–785CrossRefGoogle Scholar
  25. Hyvarinen A, Oja E (2000) Independent component analysis: algorithms and applications. Neural Netw 15(4):411–430CrossRefGoogle Scholar
  26. Kelly EJ (1986) An adaptive detection algorithm. IEEE Trans Aerosp Electon Syst 22(1):115–127CrossRefGoogle Scholar
  27. Keshava N (2003) A survey of spectral unmixing algorithms. Linc Lab J 14(1):55–78Google Scholar
  28. Keshava N, Kerekes J, Manolakis D, Shaw G (2000) An algorithm taxonomy for hyperspectral unmixing. In: Shen SS, Descour MR (eds) Algorithms for multispectral, hyperspectral, and ultraspectral imagery VI, Proceedings of SPIE, vol 4049Google Scholar
  29. Koonsanit K, Jaruskulchai C, Eiumnoh A (2012) Band selection for dimension reduction in hyperspectral image using integrated information gain and principal components analysis technique. Int J Mach Learn Comput 2(3):248CrossRefGoogle Scholar
  30. Kruse FA, Lefkoff AB, Boardman JB, Heidebrecht KB, Shapiro AT, Barloon PJ, Goetz AF (1993) The spectral image processing system (SIPS) interactive visualization and analysis of imaging spectrometer data. Remote Sens Environ 44(2/3):145–163CrossRefGoogle Scholar
  31. Kruse FA, Boardman JW, Hunnington JF (2003) Comparison of airborne hyperspectral data and EO-1 Hyperion for mineral mapping. IEEE Trans Geosci Remote Sens 41(6):1388–1400CrossRefGoogle Scholar
  32. Manolakis D, Shaw G, Keshava N (2000) Comparative analysis of hyperspectral adaptive matched filter detector. In: Proc. SPIE, vol 4049, pp 2–17Google Scholar
  33. Manolakis D, Siracusa C, Shaw G (2001) Hyperspectral subpixel target detection using the linear mixing model. IEEE Trans Geosci Remote Sens 39(7):1392–1409CrossRefGoogle Scholar
  34. Manolakis D, Marden D, Shaw G (2003) Hyperspectral image processing for automatic target detection applications. Linc Lab J 14(1):79–114Google Scholar
  35. Muhammad A, Haq I (2011) Linear unmixing and target detection of hyperspectral imagery using OSP. In: International conference on modeling, simulation and control, in proceedings of IPCSIT 2011, vol 10, pp 179–183Google Scholar
  36. Petrou M, Foschi PG (1999) Confidence in linear spectral unmixing of single pixels. IEEE Trans Geosci Remote Sens 37:624–626Google Scholar
  37. Plaza A, Martínez P, Pérez R, Plaza J (2004) A quantitative and comparative analysis of endmember extraction algorithms from hyperspectral data. IEEE Trans Geosci Remote Sens 42(3):650–663CrossRefGoogle Scholar
  38. Ramakishna B, Plaza A, Chang C-I, Ren H, Du Q, Chang C-C (2005) Spectral/spatial hyperspectral image compression. In: Motta G, Storer J (eds) Hyperspectral data compression. Springer, New YorkGoogle Scholar
  39. Ren H, Chang CI (2000) Target-constrained interference-minimized approach to subpixel target detection for hyperspectral images. Opt Eng 39(12):3138–3145. CrossRefGoogle Scholar
  40. Rhonda DP, Layne TW, Christine EB, Randolph HW (2008) An adaptive noise reduction technique for improving the utility of hyperspectral data. In: Pecora17—the future of land imaging, Going Operational November 18–20, 2008.
  41. Robey FC, Fuhrmann DR, Kelly EJ, Nitzberg R (1992) A CFAR adaptive matched filter detector. IEEE Trans Aserosp Electon Syst 38(1):208–216CrossRefGoogle Scholar
  42. Rodarmel C, Shan J (2002) Principal component analysis for hyperspectral image classification. Surv Land Inf Syst 62(2):115–122Google Scholar
  43. Schott JR (2002) Hyperspectral algorithms course notes. Class NotesGoogle Scholar
  44. Settle J (2006) On the effect of variable endmember spectra in the linear mixture model. IEEE Trans Geosci Remote Sens 44:389–396CrossRefGoogle Scholar
  45. Settle JJ, Drake NA (1993a) Linear mixing and the estimation of ground cover proportions. Int J Remote Sens 14(6):1159–1177CrossRefGoogle Scholar
  46. Settle JJ, Drake NA (1993b) Linear mixing and the estimation of ground cover proportions. Int J Remote Sens 14:1159–1177CrossRefGoogle Scholar
  47. Shaw G, Burke H (2003) Spectral imaging for remote sensing. Linc Lab J 14:3–28Google Scholar
  48. Shippert P (2004) Why use hyperspectral imagery. Photogramm Eng Remote Sens 70:377–380Google Scholar
  49. Song FY, Jiang JW (2007) ICA-based dimensionality reduction and compression of hyperspectral images. J Electron Inf Technol 29(12):2871–2875Google Scholar
  50. Wang J, Chang C-I (2006) Independent component analysis-based dimensionality reduction with applications in hyperspectral image analysis. IEEE Trans Geosci Remote Sens 44(6):1586–1600CrossRefGoogle Scholar
  51. Zabalza J, Ren J, Yang M, Zhang Y, Wang J, Marshall S, Han J (2014) Novel folded-PCA for improved feature extraction and data reduction with hyperspectral imaging and SAR in remote sensing. ISPRS J Photogramm Remote Sens 93:112–122CrossRefGoogle Scholar
  52. Zhang L, Zhang L, Tao D, Huang X, Du B (2013) Hyperspectral remote sensing image subpixel target detection based on supervised metric learning. In: IEEE transactions on geoscience and remote sensing, vol 52, no 8, pp 4955–4965.

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Civil EngineeringDelhi Technological UniversityDelhiIndia

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