Skip to main content

Advertisement

Log in

A quantitative and comparative analysis of different preprocessing implementations of DPSO: a robust endmember extraction algorithm

  • Focus
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

Linear spectral unmixing is a very important technique in hyperspectral image analysis. It contains two main steps. First, it finds spectrally unique signatures of pure ground components (called endmembers); second, it estimates their corresponding fractional abundances in each pixel. Recently, a discrete particle swarm optimization (DPSO) algorithm was introduced to accurately extract endmembers with high optimal performance. However, because of its limited feasible solution space, DPSO necessarily needs a small amount of candidate endmembers before extraction. Consequently, how to provide a suitable candidate endmember set, which has not been analyzed yet, is a critical issue in using DPSO for unmixing problem. In this study, three representative pure pixel-based methods, pixel purity index, vertex component analysis (VCA), and N-FINDR, are quantitatively compared to provide candidate endmembers for DPSO. The experiments with synthetic and real hyperspectral images indicate that VCA is the most reliable preprocessing implementation for DPSO. Further, it can be concluded that DPSO with the proposed preprocessing implementations given in this paper is robust for endmember extraction.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • Abrams MJ et al (1977) Mapping of hydrothermal alteration in the Cuprite Mining District, Nevada, using aircraft scanner images for the spectral region 0.46 to 2.36 \(\mu \)m. Geology 5(12):713–718

  • Barberis A et al (2013) Real-time implementation of the vertex component analysis algorithm on GPUs. IEEE Geosci Remote Sens Lett 10(2):251–255

    Article  Google Scholar 

  • Bioucas-Dias J et al (2012) Hyperspectral unmixing overview: geometrical, statistical, and sparse regression-based approaches. IEEE J Sel Top Appl Earth Obs Remote Sens 5(2):354–379

    Article  Google Scholar 

  • Bioucas-Dias J, Nascimento JMP (2008) Hyperspectral subspace identification. Trans Geosci Remote Sens 46(8):2435–2445

    Article  Google Scholar 

  • Boardman JW, Kruse FA (2011) Analysis of imaging spectrometer data using N-dimensional geometry and a mixture-tuned matched filtering approach. IEEE Trans Geosci Remote Sens 49(11):4138–4152

    Article  Google Scholar 

  • Chan TH et al (2009) A convex analysis based minimum-volume enclosing simplex algorithm for hyperspectral unmixing. IEEE Trans Signal Process 57(11):4418–4432

    Article  MathSciNet  Google Scholar 

  • Chang CI et al (2006) A new growing method for simplex-based endmember extraction algorithm. IEEE Trans Geosci Remote Sens 44(10):2804–2819

    Article  Google Scholar 

  • Craig MD et al (1994) Minimum-volume transforms for remotely sensed data. IEEE Trans Geosci Remote Sens 32(3):542–552

    Article  Google Scholar 

  • Goetz AFH (2009) Three decades of hyperspectral remote sensing of the Earth: a personal view. Remote Sens Environ 113(S1):S5–S16

    Article  Google Scholar 

  • Goetz AFH et al (1985) Imaging spectrometry for earth remote sensing. Science 228(4704):1147–1153

    Article  Google Scholar 

  • Green AA et al (1988) A transformation for ordering multispectral data in terms of image quality with implications for noise removal. IEEE Trans Geosci Remote Sens 26:65–74

    Article  Google Scholar 

  • Green RO et al (1998) Imaging spectroscopy and the Airborne Vsible/Infrared Imaging Spectrometer (AVIRIS). Remote Sens Environ 65(3):227–248

    Article  Google Scholar 

  • Hapke B (1981) Bidirectional reflectance spectroscopy. I. Theory. J Geophys Res 86:3039–3054

    Article  Google Scholar 

  • Ifarraguerri A, Chang CI (1999) Multispectral and hyperspectral image analysis with convex cones. IEEE Trans Geosci Remote Sens 37(2):756–770

    Article  Google Scholar 

  • Iordache MD, Bioucas-Dias J, Plaza A (2011) Sparse unmixing of hyperspectral data. IEEE Trans Geosci Remote Sens 49(6):2014–2039

    Article  Google Scholar 

  • Iordache MD, Bioucas-Dias J, Plaza A (2012) Total variation spatial regularization for sparse hyperspectral unmixing. IEEE Trans Geosci Remote Sens 50(11):4484–4502

    Article  Google Scholar 

  • Jacquemoud S, Baret F (1990) PROSPECT: a model of leaf optical properties spectra. Remote Sens Environ 34(2):75–91

    Article  Google Scholar 

  • Kennedy J (1997) The particle swarm: social adaptation of knowledge. In: IEEE international conference on evolutionary computation, pp 303–308

  • Kennedy J, Eberhart R (1995) Particle swarm optimization. IEEE Int Conf Neural Netw 4:1942–1948

    Google Scholar 

  • Keshava N, Mustard JF (2002) Spectral unmixing. IEEE Signal Process Mag 19(1):44–57

    Article  Google Scholar 

  • Kuo RJ, Wang MJ, Huang TW (2011) An application of particle swarm optimization algorithm to clustering analysis. Soft Comput 15(3):533–542

    Article  Google Scholar 

  • Langdon WB (2011) Graphics processing units and genetic programming: an overview. Soft Comput 15(8):1657–1669

    Article  Google Scholar 

  • Li J, Bioucas-Dias J (2008) Minimum volume simplex analysis: a fast algorithm to unmix hyperspectral data. In: Proceedings of IEEE international conferance on geoscience remote sensing (IGARSS), vol 3, pp 250–253

  • Liu JM, Zhang JS (2012) A new maximum simplex volume method based on householder transformation for endmember extraction. IEEE Trans Geosci Remote Sens 50(1):104–118

    Article  Google Scholar 

  • Luo WF, Zhang B, Jia XP (2012) New improvements in parallel implementation of N-FINDR algorithm. IEEE Trans Geosci Remote Sens 50(10):3648–3659

    Article  Google Scholar 

  • Martín G, Plaza A (2012) Spatial-spectral preprocessing prior to endmember identification and unmixing of remotely sensed hyperspectral data. IEEE J Sel Top Appl Earth Obs Remote Sens 5(2):380–395

    Article  Google Scholar 

  • Nascimento JMP, Bioucas-Dias J (2005) Vertex component analysis: a fast algorithm to unmix hyperspectral data. IEEE Trans Geosci Remote Sens 43(4):898–910

    Article  Google Scholar 

  • Plaza A et al (2002) Spatial/spectral endmember extraction by multidimensional morphological operations. IEEE Trans Geosci Remote Sens 40(9):2025–2041

    Article  Google Scholar 

  • Plaza A et al (2004) A quantitative and comparative analysis of endmember extraction algorithms from hyperspectral data. IEEE Trans Geosci Remote Sens 42(3):650–663

    Article  Google Scholar 

  • Plaza A et al (2006a) Commodity cluster-based parallel processing of hyperspectral imagery. J Parallel Distrib Comput 66(3):345–358

  • Plaza A et al (2006b) Parallel implementation of endmember extraction algorithms from hyperspectral data. IEEE Geosci Remote Sens Lett 3(3):334–338

    Article  MathSciNet  Google Scholar 

  • Plaza A et al (2011a) Parallel hyperspectral image and signal processing. IEEE Signal Process Mag 28(3):119–126

    Article  MathSciNet  Google Scholar 

  • Plaza A et al (2011b) High performance computing for hyperspectral remote sensing. IEEE J Sel Top Appl Earth Obs Remote Sens 4(3):528–544

    Article  MathSciNet  Google Scholar 

  • Quintano C et al (2012) Spectral unmixing. Int J Remote Sens 33(17):5307–5340

    Article  Google Scholar 

  • Rogge DM et al (2007) Integration of spatial–spectral information for the improved extraction of endmembers. Remote Sens Environ 110(3):287–303

    Article  Google Scholar 

  • Shi YH, Eberhart R (1998) A modified particle swarm optimizer. In: The 1998 IEEE international conference on evolutionary computation proceedings, IEEE world congress on computational intelligence, pp 69–73

  • Tang EK et al (2006) Gene selection algorithms for microarray data based on least squares support vector machine. BMC Bioinform 7(1):95

    Article  Google Scholar 

  • Tassopoulos IX, Beligiannis GN (2012) Using particle swarm optimization to solve effectively the school timetabling problem. Soft Comput 16(7):1229–1252

    Article  Google Scholar 

  • Winter ME (2004) A proof of the N-FINDR algorithm for the automated detection of endmembers in a hyperspectral image. Proc SPIE 5425:31–41

    Article  Google Scholar 

  • Wang R et al (2012) Feature selection for MAUC-oriented classification systems. Neurocomputing 89:39–54

    Article  Google Scholar 

  • Zhang B et al (2011a) Endmember extraction of hyperspectral remote sensing images based on the discrete particle swarm optimization algorithm. IEEE Trans Geosci Remote Sens 49(11):4173–4176

  • Zhang B et al (2011b) Endmember extraction of hyperspectral remote sensing images based on the Ant Colony Optimization (ACO) algorithm. IEEE Trans Geosci Remote Sens 49(7):2635–2646

    Article  Google Scholar 

  • Zhang B et al (2013) Improvements in the ant colony optimization algorithm for endmember extraction from hyperspectral images. IEEE J Sel Top Appl Earth Obs Remote Sens 6(2):522–530

    Article  Google Scholar 

  • Zhong YF, Feng RY, Zhang LP (2013a) Non-local sparse unmixing for hyperspectral remote sensing imagery. IEEE J Sel Top Appl Earth Obs Remote Sens 99:1–21

  • Zhong YF, Zhao L, Zhang LP (2013b) An adaptive differential evolution endmember extraction algorithm for hyperspectral remote sensing imagery. IEEE Geosci Remote Sens Lett 99:1–5

  • Zortea M, Plaza A (2009) A quantitative and comparative analysis of different implementations of N-FINDR: a fast endmember extraction algorithm. IEEE Geosci Remote Sens Lett 6(4):787–791

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by the Key Research Program of the Chinese Academy of Sciences (KZZD-EWTZ-18), the National Natural Science Foundation of China (No. 41325004 and No. 41301384), and the Interdisciplinary and Collaborative S&T Innovation Research Team on Advance Earth Observation System, CAS.

Conflict of interest

The authors declare that they have no conflict of interest.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yuanfeng Wu.

Additional information

Communicated by Y.-S. Ong.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gao, L., Zhuang, L., Wu, Y. et al. A quantitative and comparative analysis of different preprocessing implementations of DPSO: a robust endmember extraction algorithm. Soft Comput 20, 4669–4683 (2016). https://doi.org/10.1007/s00500-014-1507-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-014-1507-2

Keywords

Navigation