Abstract
The application of hyperspectral imaging to detect soil contaminations is considered with respect to contamination assessment within urban areas. An optimal selection of hyperspectral imagery spectral bands is proposed. There is no reason to process all spectral bands as usually the 10–30 most informative ones are sufficient. A new criterion is introduced that incorporates such fundamental properties of hyperspectral imagery such as spatial resolution, mutual spectral information and signal-to-noise ratio. Algorithms for hyperspectral imagery processing and analysis were adapted for geochemical contamination detection. EO-1/Hyperion hyperspectral imagery was applied to central Kiev, Ukraine, and estimations of geochemical contamination of this area were mapped.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Aarts EHL, Lenstra JK (1997) Local search in combinatorial optimization. Wiley, Chichester
Bonifazi G, Piga L, Serranti S, Menesatti P (2005) Hyperspectral imaging based techniques in contaminated soils characterization. Proc SPIE 5996:1117
Callan SJ, Thomas JM (2007) Environmental economics & management: theory, policy, and applications. Thomson South Western, Mason
Gerasimov AV (2008) Environmental security of modern Russia: a policy of enforcement. Peoples’ Friendship University of Russia, Moscow
Govender M, Chetty K, Bulcock H (2007) A review of hyperspectral remote sensing and its application in vegetation and water resource studies. Water SA 33(2):145–152
Jarboe JF (2002) The threat of eco-terrorism. http://www.fbi.gov/congress/congress02/jarboe021202.html
Jones DR (2001) A taxonomy of global optimization methods based on response surfaces. J Glob Optim 21:345–383
Lebedev VV (2002) Development of regional geoinformation complexes and solution of applied problems based on aerospace information. Science, Moscow
Lukin VV (2009) Processing of multichannel RS data for environment monitoring. In: Proceedings of NATO advanced research workshop on geographical information processing and visual analytics for environmental security, Trento, Italy
Lukin VV, Abramov SK, Ponomarenko NN, Vozel B, Chehdi K (2006) Methods for blind evaluation of noise variance in multichannel optical and radar images. Telecommun Radioeng 65(6):527–556
Lukin VV, Ponomarenko NN, Zelensky AA, Kurekin AA, Lever K (2008) Compression and classification of noisy multichannel remote sensing images. In: Proceedings of XIV SPIE conference on image and signal processing for remote sensing, Cardiff, UK
Lukin VV, Ponomarenko NN, Zriakhov MS, Krivenko SS, Zelensky AA, Popov MA, Stankevich SA, Kovalchuk SP, Titarenko OV (2009) Methods for remote sensing hyperspectral imagery processing and compression (Russian). In: Proceedings of 8th international conference on modern information technologies in ecological security, natural resources and disasters management, Rybachie, Ukraine
Lysiany NN (1990) Complex study of the detected contamination zones and operational observation within Kiev habitat and industrial agglomeration and near areas (Russian). North-Ukraine Geology, Kiev
Nocedal J, Wright SJ (1999) Numerical optimization. Springer, Berlin
Pascucci S, Belviso C, Cavalli RM, Laneve G, Misurovic A, Perrino C, Pignatti S (2009) Red mud soil contamination near an urban settlement analyzed by airborne hyperspectral remote sensing. In: International geoscience and remote sensing symposium, Cape Town, South Africa, vol 4, pp 893–896
Penders M, Ronald D (2008) The hanging chads of international environmental law: towards the better use of technology and information management to detect violations of international agreements, combat transnational crime, and reform environmental law and trade. Environmental Security International. http://www.esisecurity.com/publication_1.html. Accessed 14 June 2010
Ponomarenko NN, Lukin VV, Zriakhov MS, Kaarna A, Astola J (2008) Automatic approaches to on-land/on-board filtering and lossy compression of AVIRIS images. Proc IGARSS 3:254–257
Ponomarenko NN, Krivenko SS, Lukin VV, Egiazarian KO (2009) Visual quality of lossy compressed images. In: Proceedings of 10th international conference on the experience of designing and application of CAD systems in microelectronics, CADSM’2009, Svalyava, Ukraine
Ponomarenko NN, Lukin VV, Egiazarian KO, Astola JT (2010) A method for blind estimation of spatially correlated noise characteristics. Proc SPIE 7532:3208
Popov MA, Stankevich SA, Lischenko LP, Podorvan VN (2007a) Mapping of technogenic contaminations of urban area using hyperspectral imagery. Thesis of Polish-Ukrainian workshop on space applications, Warsaw, Poland
Popov MA, Stankevich SA, Podorvan VN, Sholonik OV (2007b) Robust land cover classification using hyperspectral imagery (Ukrainian). In: Proceedings of international conference on modern problems of development and efficient exploitation of Ukraine Joint Information Space in administrative decision making, Kiev, Ukraine
Prelat AE, Wieser JD, Gazzani RO, Re Kuhl G, Rocchi E (2001) Hyperspectral remote sensing of energy resources exploration and environmental applications. In: World Energy Council 18th Congress, Buenos Aires, Argentina
Salem F, Kafatos M, El-Ghazawi T, Gomez R, Yang R (2005) Hyperspectral image assessment of oil-contaminated wetland. Int J Remote Sens 26(4):811–821
Stankevich SA (2006) Quantitative estimation of hyperspectral aerospace imagery informativity for the remote sensing thematic tasks (in Ukrainian). Proc NAS Ukraine 10:136–139
Stankevich SA (2007) Hyperspectral aerospace imagery spectral bands optimal selection for the remote sensing thematic tasks (in Russian). Space Sci Technol 13(2):25–28
Tang X, Pearlman WA (2006) Three-dimensional wavelet-based compression of hyperspectral images. In: Notta G, Rizzo F, Storer JA (eds) Hyperspectral data compression. Springer, New York
UNEP (2003) Environment in Iraq: UNEP Progress Report, UNEP, Geneva
UNEP (2007) Environmental survey of Ogoniland: UNEP Project Report, UNEP, Nairobi
Yu G, Vladimirova T, Sweeting MN (2009) Image compression systems on board satellites. Acta Astronaut 64:988–1005
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer Science+Business Media B.V.
About this paper
Cite this paper
Popov, M.A., Stankevich, S.A., Lischenko, L.P., Lukin, V.V., Ponomarenko, N.N. (2011). Processing of Hyperspectral Imagery for Contamination Detection in Urban Areas. In: Alpas, H., Berkowicz, S., Ermakova, I. (eds) Environmental Security and Ecoterrorism. NATO Science for Peace and Security Series C: Environmental Security. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-1235-5_12
Download citation
DOI: https://doi.org/10.1007/978-94-007-1235-5_12
Published:
Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-007-1234-8
Online ISBN: 978-94-007-1235-5
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)