article

Spatio-spectral fusion of satellite images based on dictionary-pair learning

Authors:

Hankui ZhangAuthors Info & Claims

Information Fusion, Volume 18

Pages 148 - 160

https://doi.org/10.1016/j.inffus.2013.08.005

Published: 01 July 2014 Publication History

Abstract

This paper proposes a novel spatial and spectral fusion method for satellite multispectral and hyperspectral (or high-spectral) images based on dictionary-pair learning. By combining the spectral information from sensors with low spatial resolution but high spectral resolution (LSHS) and the spatial information from sensors with high spatial resolution but low spectral resolution (HSLS), this method aims to generate fused data with both high spatial and spectral resolution. Based on the sparse non-negative matrix factorization technique, this method first extracts spectral bases of LSHS and HSLS images by making full use of the rich spectral information in LSHS data. The spectral bases of these two categories data then formulate a dictionary-pair due to their correspondence in representing each pixel spectra of LSHS data and HSLS data, respectively. Subsequently, the LSHS image is spatial unmixed by representing the HSLS image with respect to the corresponding learned dictionary to derive its representation coefficients. Combining the spectral bases of LSHS data and the representation coefficients of HSLS data, fused data are finally derived which are characterized by the spectral resolution of LSHS data and the spatial resolution of HSLS data. The experiments are carried out by comparing the proposed method with two representative methods on both simulation data and actual satellite images, including the fusion of Landsat/ETM+ and Aqua/MODIS data and the fusion of EO-1/Hyperion and SPOT5/HRG multispectral images. By visually comparing the fusion results and quantitatively evaluating them in term of several measurement indices, it can be concluded that the proposed method is effective in preserving both the spectral information and spatial details and performs better than the comparison approaches.

References

[1]

Weydahl, D.J., Bretar, F. and Bjerke, P., Comparison of RADARSAT-1 and IKONOS satellite images for urban features detection. Inform. Fusion. v6. 243-249.

[2]

Landgrebe, D., Hyperspectral image data analysis. IEEE Signal Process. Mag. v19. 17-28.

[3]

Aiazzi, B., Baronti, S., Lotti, F. and Selva, M., A comparison between global and context-adaptive pansharpening of multispectral images. IEEE Geosci. Remote Sens. Lett. v6. 302-306.

[4]

Amro, I., Mateos, J., Vega, M., Molina, R. and Katsaggelos, A.K., A survey of classical methods and new trends in pansharpening of multispectral images. EURASIP J. Adv. Signal Process.

[5]

Zhukov, B., Oertel, D., Lanzl, F. and Reinhackel, G., Unmixing-based multisensor multiresolution image fusion. IEEE Trans. Geosci. Remote Sens. v37. 1212-1226.

[6]

Minghelli-Roman, A., Mangolini, M., Petit, M. and Polidori, L., Spatial resolution improvement of MeRIS images by fusion with TM images. IEEE Trans. Geosci. Remote Sens. v39. 1533-1536.

[7]

Zurita-Milla, R., Clevers, J. and Schaepman, M.E., Unmixing-based Landsat TM and MERIS FR data fusion. IEEE Geosci. Remote Sens. Lett. v5. 453-457.

[8]

Mezned, N., Abdeljaoued, S. and Boussema, M.R., A comparative study for unmixing based Landsat ETM + and ASTER image fusion. Int. J. Appl. Earth Obs. Geoinf. v12. 131-137.

[9]

Amorós-López, J., Gómez-Chova, L., Alonso, L., Guanter, L., Moreno, J. and Camps-Valls, G., Regularized multiresolution spatial unmixing for ENVISAT/MERIS and landsat/TM image fusion. IEEE Geosci. Remote Sens. Lett. v8. 844-848.

[10]

Hardie, R.C. and Eismann, M.T., MAP estimation for hyperspectral image resolution enhancement using an auxiliary sensor. IEEE Trans. Image Process. v13. 1174-1184.

Digital Library

[11]

Eismann, M.T. and Hardie, R.C., Application of the stochastic mixing model to hyperspectral resolution enhancement. IEEE Trans. Geosci. Remote Sens. v42. 1924-1933.

[12]

Eismann, M.T. and Hardie, R.C., Hyperspectral Resolution Enhancement Using high-resolution multispectral imagery with arbitrary response functions. IEEE Trans. Geosci. Remote Sens. v43. 455-465.

[13]

Zhao, Y., Yang, J., Zhang, Q., Song, L., Cheng, Y. and Pan, Q., Hyperspectral imagery super-resolution by sparse representation and spectral regularization. EURASIP J. Adv. Signal Process. v2011 i1. 1-10.

[14]

Keshava, N. and Mustard, J.F., Spectral unmixing. IEEE Signal Proc. Mag. v19. 44-57.

[15]

Plaza, A., Martínez, P., Pérez, R. and Plaza, J., A quantitative and comparative analysis of endmember extraction algorithms from hyperspectral data. IEEE Geosci. Remote Sens. Lett. v42. 650-663.

[16]

Iordache, M.-D., Bioucas-Dias, J.M. and Plaza, A., Sparse unmixing of hyperspectral data. IEEE Trans. Geosci. Remote Sens. v49 i6. 2014-2039.

[17]

Toši¿c, I. and Prossard, P., Dictionary learning. IEEE Signal Process. Mag. v28. 27-38.

[18]

Aharon, M., Elad, M. and Bruckstein, A., K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans. Signal Process. v54. 4311-4322.

[19]

Rubinstein, R., Bruckstein, A. and Elad, M., Dictionaries for sparse representation modeling. IEEE Proc. - Special Issue Appl. Sparse Represent. Compress. Sens. v98. 1045-1057.

[20]

Tropp, J.A., Greed is good: algorithmic results for sparse approximation. IEEE Trans. Inform. Theory. v50. 2231-2242.

[21]

Tibshirani, R., Regression shrinkage and selection via the lasso. J. R. Stat. Soc. Ser. B (Method.). v58. 267-288.

[22]

Tropp, J.A. and Wright, S.J., Computational methods for sparse solution of linear inverse problems. Proc. IEEE. v98. 948-958.

[23]

Yang, J., Wright, J., Huang, T. and Ma, Y., Image super-resolution via sparse representation. IEEE Trans. Image Process. v19. 2861-2873.

Digital Library

[24]

R. Zeyde, M. Elad, M. Protter, On Single Image Scale-Up using Sparse-Representations, Curves & Surfaces, Avignonm, France, 2010, pp. 711-730.

[25]

Rubinstein, R., Bruckstein, A.M. and Elad, M., Dictionaries for sparse representation modeling. IEEE Proc. v98. 1045-1057.

[26]

Lee, D.D. and Seung, H.S., Learning the parts of objects by nonnegative matrix factorization. Nature. v401. 788-791.

[27]

Hoyer, P.O., Non-negative Matrix Factorization with Sparseness Constraints. J. Mach. Learning Res. v5. 1457-1468.

[28]

Schowengerdt, R.A., Remote sensing: models and methods for image processing. 2007. Academic Press, New York.

[29]

Song, H. and Huang, B., Spatiotemporal satellite image fusion through one-pair image learning. IEEE Trans. Geosci. Remote Sens. v51 i4. 1883-1896.

[30]

Estimation of number of spectrally distinct signal sources in hyperspectral imagery. IEEE Trans. Geosci. Remote Sens. v42. 608-619.

[31]

Khan, M.M., Alparone, L. and Chanussot, J., Pansharpening quality assessment using the modulation transfer functions of instruments. IEEE Trans. Geosci. Remote Sens. v47. 3880-3891.

[32]

R.H. Yuhas, A.F.H. Goetz, J.W. Boardman, Discrimination among semi-arid landscape endmembers using the spectral angle mapper (SAM) algorithm, in: Proc. Summaries 3rd Annu. JPL Airborne Geosci. Workshop, 1992, pp. 147-149.

[33]

Wang, Z., Bovik, A.C., Sheikh, H.R. and Simoncelli, E.P., Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. v13. 600-612.

Digital Library

[34]

Alparone, L., Aiazzi, B., Baronti, S., Garzelli, A., Nencini, F. and Selva, M., Multispectral and panchromatic data fusion assessment without reference. Photogramm. Eng. Remote Sens. v74. 193-200.

[35]

Yasuma, F., Mitsunaga, T., Iso, D. and Nayar, S.K., Generalized assorted pixel camera: postcapture control of resolution, dynamic range, and spectrum. IEEE Trans. IP. v19. 2241-2253.

Digital Library

[36]

Denoising of hyperspectral imagery using principal component analysis and wavelet shrikage. IEEE Trans. Geosci. Remote Sens. v49. 973-980.

Cited By

Fang LZhuo HLi S(2018)Super-resolution of hyperspectral image via superpixel-based sparse representationNeurocomputing10.1016/j.neucom.2017.08.019273:C(171-177)Online publication date: 17-Jan-2018
https://dl.acm.org/doi/10.1016/j.neucom.2017.08.019
Li SKang XFang LHu JYin H(2017)Pixel-level image fusionInformation Fusion10.1016/j.inffus.2016.05.00433:C(100-112)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.1016/j.inffus.2016.05.004
Veganzones MSimoes MLicciardi GYokoya NBioucas-Dias JChanussot J(2016)Hyperspectral Super-Resolution of Locally Low Rank Images From Complementary Multisource DataIEEE Transactions on Image Processing10.1109/TIP.2015.249626325:1(274-288)Online publication date: 1-Jan-2016
https://dl.acm.org/doi/10.1109/TIP.2015.2496263
Show More Cited By

Index Terms

Spatio-spectral fusion of satellite images based on dictionary-pair learning
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
      2. Computer vision tasks
        Scene understanding
  2. Machine learning

Index terms have been assigned to the content through auto-classification.

Recommendations

Hierarchical spatio-spectral fusion for hyperspectral image super resolution via sparse representation and pre-trained deep model
Abstract
Fusing a hyperspectral image (HSI) with a high resolution multispectral image (MSI) has been a highly attractive and effective approach for improving the spatial resolution of HSIs. However, most existing spatio-spectral fusion methods ...
Adaptive Spatial-Spectral Dictionary Learning for Hyperspectral Image Restoration

Hyperspectral imaging is beneficial in a diverse range of applications from diagnostic medicine, to agriculture, to surveillance to name a few. However, hyperspectral images often suffer from degradation such as noise and low resolution. In this paper, ...
Remote sensing of urban vegetation life form by spectral mixture analysis of high-resolution IKONOS satellite images

This paper evaluates the techniques of linear spectral unmixing (LSU), comparing high-and medium-resolution images for their ability to obtain separate estimates of tree and grassy surfaces in urban areas. It demonstrates that, unlike on medium-...

Comments

Information & Contributors

Information

Published In

cover image Information Fusion

Information Fusion Volume 18, Issue

July, 2014

202 pages

ISSN:1566-2535

Issue’s Table of Contents

Copyright © Elsevier B.V. © 2013.

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 July 2014

Author Tags

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

4
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 12 Aug 2024

Other Metrics

View Author Metrics

Citations

Cited By

Fang LZhuo HLi S(2018)Super-resolution of hyperspectral image via superpixel-based sparse representationNeurocomputing10.1016/j.neucom.2017.08.019273:C(171-177)Online publication date: 17-Jan-2018
https://dl.acm.org/doi/10.1016/j.neucom.2017.08.019
Li SKang XFang LHu JYin H(2017)Pixel-level image fusionInformation Fusion10.1016/j.inffus.2016.05.00433:C(100-112)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.1016/j.inffus.2016.05.004
Veganzones MSimoes MLicciardi GYokoya NBioucas-Dias JChanussot J(2016)Hyperspectral Super-Resolution of Locally Low Rank Images From Complementary Multisource DataIEEE Transactions on Image Processing10.1109/TIP.2015.249626325:1(274-288)Online publication date: 1-Jan-2016
https://dl.acm.org/doi/10.1109/TIP.2015.2496263
Son CLee KChoo H(2015)Inverse color to black-and-white halftone conversion via dictionary learning and color mappingInformation Sciences: an International Journal10.1016/j.ins.2014.12.002299:C(1-19)Online publication date: 1-Apr-2015
https://dl.acm.org/doi/10.1016/j.ins.2014.12.002

View Options

View options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Issue’s Table of Contents