Advertisement

Computing

, Volume 100, Issue 11, pp 1175–1188 | Cite as

Assessment of the spectral quality of fused images using the CIEDE2000 distance

  • Dionisio Rodríguez-Esparragón
  • Javier Marcello
  • Consuelo Gonzalo-Martín
  • Ángel García-Pedrero
  • Francisco Eugenio
Article

Abstract

Image fusion (pan-sharpening) plays an important role in remote sensing applications. Mainly, this process allows to obtain images of high spatial and spectral resolution. However, pan-sharpened images usually present spectral and spatial distortion when comparing with the source images. Because of this, the evaluation of the spectral quality of pan-sharpened images is a fundamental subject to optimize and compare the results of different algorithms. Several assessments of spectral quality have been described in the scientific literature. However, no consensus has been reached on which one describes optimally the spectral distortion in the image. In addition, its performance from the point of view of perceived spectral quality has not been addressed. The aim of this paper is to explore the use of CIEDE2000 distance to evaluate the spectral quality of the fused images. To do this, a database containing remote sensing imagery and its fusion products was created. The spectral quality of the imagery on the database was evaluated using both common quantitative indices and CIEDE2000. With the purpose of determining the relationship between the quantitative indices of spectral quality and the subjective perception of the spectral quality of the merged image, these results were compared to the qualitative assessment provided by a mean opinion score test.

Keywords

Image fusion Image quality Quality indicators CIEDE2000 Pan-sharpening 

Mathematics Subject Classification

94A08 

Notes

Acknowledgements

This work has been supported by the project ARTeMISat-2: Advanced Processing of Remote Sensing Data for Monitoring and Sustainable Management of Marine and Terrestrial Resources in Vulnerable Ecosystems (CTM2016-77733-R), funded by the Spanish Agencia Estatal de Investigación (AEI) and Fondo Europeo de Desarrollo Regional (FEDER).

References

  1. 1.
    Alimuddin I, Sumantyo JTS, Kuze H et al (2012) Assessment of pan-sharpening methods applied to image fusion of remotely sensed multi-band data. Int J Appl Earth Obs Geoinf 18:165–175CrossRefGoogle Scholar
  2. 2.
    Cheng G, Han J (2016) A survey on object detection in optical remote sensing images. ISPRS J Photogramm Remote Sens 117:11–28CrossRefGoogle Scholar
  3. 3.
    El-Mezouar MC, Kpalma K, Taleb N, Ronsin J (2014) A pan-sharpening based on the non-subsampled contourlet transform: application to worldview-2 imagery. IEEE J Sel Top Appl Earth Obs Remote Sens 7(5):1806–1815CrossRefGoogle Scholar
  4. 4.
    Gu W, Lv Z, Hao M (2017) Change detection method for remote sensing images based on an improved markov random field. Multimed Tools Appl 76(17):17,719–17,734CrossRefGoogle Scholar
  5. 5.
    ITU-R R (2002) Methodology for the subjective assessment of the quality of television pictures. https://www.itu.int/dms_pubrec/itu-r/rec/bt/RREC-BT.500-11-200206-S!!PDF-E.pdf
  6. 6.
    Li S, Kang X, Fang L, Hu J, Yin H (2017) Pixel-level image fusion: a survey of the state of the art. Inf Fus 33:100–112CrossRefGoogle Scholar
  7. 7.
    Lillo-Saavedra M, Gonzalo C (2006) Spectral or spatial quality for fused satellite imagery? a trade-off solution using the wavelet à trous algorithm. Int J Remote Sens 27(7):1453–1464CrossRefGoogle Scholar
  8. 8.
    Lillo-Saavedra M, Gonzalo C, Lagos O (2011) Toward reduction of artifacts in fused images. Int J Appl Earth Obs Geoinf 13(3):368–375CrossRefGoogle Scholar
  9. 9.
    Luo MR, Cui G, Rigg B (2001) The development of the cie 2000 colour-difference formula: Ciede 2000. Color Res Appl 26(5):340–350CrossRefGoogle Scholar
  10. 10.
    Ma Y, Chen L, Liu P, Lu K (2016) Parallel programing templates for remote sensing image processing on gpu architectures: design and implementation. Computing 98(1–2):7–33MathSciNetCrossRefGoogle Scholar
  11. 11.
    Marcello J, Medina A, Eugenio F (2013) Evaluation of spatial and spectral effectiveness of pixel-level fusion techniques. IEEE Geosci Remote Sens Lett 10(3):432–436CrossRefGoogle Scholar
  12. 12.
    Masini N, Lasaponara R (2017) Sensing the past from space: approaches to site detection. In: Masini N, Soldovieri F (eds) Sensing the past. Geotechnologies and the environment, vol 16. Springer, ChamCrossRefGoogle Scholar
  13. 13.
    Pushparaj J, Hegde AV (2017) Evaluation of pan-sharpening methods for spatial and spectral quality. Appl Geomat 9(1):1–12CrossRefGoogle Scholar
  14. 14.
    Rodriguez-Esparragon D, Marcello-Ruiz J, Medina-Machín A, Eugenio-Gonzalez F, Gonzalo-Martín C, Garcia-Pedrero A (2014) Evaluation of the performance of spatial assessments of pansharpened images. In: Geoscience and remote sensing symposium (IGARSS), 2014 IEEE international, IEEE, pp 1619–1622Google Scholar
  15. 15.
    Rodriguez-Esparragon D, Marcello J, Eugenio-Gonzalez F, Garcia-Pedrero A, Gonzalo-Martin C (2017) Assessment of the spectral quality of fused images using the ciede2000 distance. In: 2017 international conference and workshop on bioinspired intelligence (IWOBI), pp 1–4.  https://doi.org/10.1109/IWOBI.2017.7985536
  16. 16.
    Shi Y, Yang X, Cheng T (2014) Pansharpening of multispectral images using the nonseparable framelet lifting transform with high vanishing moments. Inf Fus 20:213–224CrossRefGoogle Scholar
  17. 17.
    Sun W, Chen B, Messinger D (2014) Nearest-neighbor diffusion-based pan-sharpening algorithm for spectral images. Opt Eng 53(1):013107CrossRefGoogle Scholar
  18. 18.
    Wang Z, Bovik AC (2006) Modern image quality assessment. Synth Lectures Image Video Multimed Process 2(1):1–156CrossRefGoogle Scholar
  19. 19.
    Yang Y, Park DS, Huang S, Rao N (2010) Medical image fusion via an effective wavelet-based approach. EURASIP J Adv Signal Process 1:579341CrossRefGoogle Scholar
  20. 20.
    Yang Y, Ming J, Yu N (2012) Color image quality assessment based on ciede2000. Adv Multimed 2012:11Google Scholar

Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Instituto de Oceanografía y Cambio Global, IOCAGUniversidad de Las Palmas de Gran Canaria, ULPGCLas Palmas de Gran CanariaSpain
  2. 2.School of Computer EngineeringUniversidad Politécnica de Madrid (UPM)MadridSpain
  3. 3.EiFAB-iUFORUniversidad de ValladolidSoriaSpain

Personalised recommendations