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Fusion of aerial gamma-ray survey and remote sensing data for a deeper understanding of radionuclide fate after radiological incidents: examples from the Fukushima Dai-Ichi response

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Abstract

We consider fusion of heterogeneous data, consisting of multispectral imagery collected from the WorldView-2 satellite and aerial gamma-ray spectra collected during the NNSA Aerial Measuring System response to the Fukushima Dai-ichi Nuclear Power Plant crisis. These data are analyzed using the non-linear dimension reduction technique of diffusion maps. The data is then used for classification of regions in the scene. A strong spatial coherence is observed in the gamma-ray data that is closely coupled to the underlying terrain classification obtained from the multispectral data, indicating correlation between these a priori uncorrelated measurements.

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References

  1. Coifman RR, Lafon S, Lee A, Maggioni M, Nadler B, Warner F, Zucker S (2005) Geometric diffusions as a tool for harmonic analysis and structure definition of data: diffusion maps. Proc Natl Acad Sci 102:7426–7431

    Article  CAS  Google Scholar 

  2. Lafon S, Keller Y, Coifman RR (2006) Data fusion and multicue data matching by diffusion maps. IEEE T Pattern Anal 28:1784–1797

    Article  Google Scholar 

  3. Benedetto JJ, Cloninger A, Czaja W, Doster T, Kochersberger K, Manning B, McCullough T, McLean M (2014) Operator-based integration of information in multimodal radiological search mission with applications to anomaly detection. Proc SPIE 9073:90731A

    Article  Google Scholar 

  4. Smith PEJ, Milton O, Adams JB (1986) Quantitative determination of mineral types and abundances from reflectance spectra using principal components analysis. J Geophys Res 90:C797–C804

    Article  Google Scholar 

  5. Ramos F, Dickson B, Kumar S (2007) Denoising aerial gamma-ray surveying through non-linear dimensionality reduction. J Field Robot 24:849–861

    Article  Google Scholar 

  6. Jones AE, Turner P, Zimmerman CH, Goulermas JY (2014) Classification of spent reactor fuel for nuclear forensics. Anal Chem 86:5399–5405

    Article  CAS  Google Scholar 

  7. Heydon A, Cooper C, Thompson P, Turner PG, Gregg R, Hesketh K, Goulermas JY, Jones AE (2014) The UK’s contribution to the galaxy serpent table top exercise. J Nucl Mat 42:55–64

    Google Scholar 

  8. Bachmann CM, Ainsworth TL, Fusina RA (2005) Exploiting manifold geometry in hyperspectral imagery. IEEE T Geosci Remote 43:441–454

    Article  Google Scholar 

  9. Sun W, Halevy A, Benedetto JJ, Czaja W, Liu C, Wu H, Shi B, Li W (2014) UL-Isomap based nonlinear dimensionality reduction for hyperspectral imagery classification. ISPRS J Photogramm 89:25–36

    Article  Google Scholar 

  10. Sun W, Halevy A, Benedetto JJ, Czaja W, Li W, Liu C, Shi B, Wang R (2014) Nonlinear dimensionality reduction via the enh-ltsa method for hyperspectral image classification. IEEE J Sel Top App 7:375–388

    Google Scholar 

  11. Belkin M, Niyogi P (2003) Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput 1396:1373–1396

    Article  Google Scholar 

  12. Czaja W, Ehler M (2013) Schroedinger eigenmaps for the analysis of bio-medical data. IEEE T Pattern Anal 35:1274–1280

    Article  Google Scholar 

  13. van der Maaten LJP, Postma EO, van den Herik HJ (2009) Dimensionality reduction: a comparative review. Tilburg University Technical Report TiCC-TR 2009-005

  14. van der Maaten LJP, Hinton GE (2008) Visualizing High-Dimensional Data Using t-SNE. J Mach Learn Res 9:2579–2605

    Google Scholar 

  15. Cloninger A, Czaja W, Doster T (2014) Operator analysis and diffusion based embeddings for heterogeneous data fusion. Int Geosci Remote Se 2014:1249–1252

    Google Scholar 

Download references

Acknowledgments

This work was supported in part by a research grant From the Defense Threat Reduction Agency, Basic Science for Combatting Weapons of Mass Destruction, HDTRA1-13-1-0015 “Harmonic Analysis Methods for Autonomous Radiological Search: A Data Driven Approach”. The authors gratefully acknowledge Chae Clark and Dan Weinberg from the University of Maryland for valuable insights into the computational techniques used in the data analysis.

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Correspondence to Lance McLean.

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Czaja, W., Manning, B., McLean, L. et al. Fusion of aerial gamma-ray survey and remote sensing data for a deeper understanding of radionuclide fate after radiological incidents: examples from the Fukushima Dai-Ichi response. J Radioanal Nucl Chem 307, 2397–2401 (2016). https://doi.org/10.1007/s10967-015-4650-z

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