Abstract
The developed hardware and software system for the recognition of natural and man-made objects based on the airborne hyperspectral sensing implements flight tasks on selected survey routes and computational procedures for solving applied problems that occur in data processing. The basics of object recognition based on obtained images of high spectral and spatial resolution in mathematical terms of sets of sites and labels and the basics of interrelations between separate resolution elements (pixels) for selected object classes are presented. Features of energy minimization of the processed scene are depicted as a target function of the optimization of computation and regularization of the solution of the considered problems as a theoretical basis for distinguishing between classes of objects in the presence of boundaries between them. Examples of the formation of information layers of recorded spectra for selected “pure species” of pine and birch forests are cited, with the separation of illuminated and shaded pixels, which increases the accuracy of object recognition in the processing of the images.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Besag, J. and Green, P.J., Spatial statistics and bayesian computation, J. R. Stat. Soc. Ser. B, 1993, vol. 55, no. 1, pp. 25–37.
Bolton, J. and Gader, P., Random set framework for context-based classification with hyperspectral imagery, IEEE Trans. Geosci. Remote Sens., 2009, vol. 47, no. 11, pp. 3810–3821.
Cross, G.C. and Jain, A.K., Markov random field texture models, IEEE Trans. Pattern Anal. Mach. Intell., 1983, vol. 5, no. 1, pp. 25–39.
Curran, P.J., Foody, G.M., Kondratyev, K.Ya., Kozoderov, V.V., and Fedchenko, P.P., Remote Sensing of Soils and Vegetation in the USSR, London: Taylor and Francis, 1990.
Dmitriev, E.V., Classification of the forest cover of the Tver region on the basis of hyperspectral aerial imagery, Issled. Zemli Kosmosa, 2013, no. 3, pp. 22–32.
Duda, R. and Hart, P., Pattern Classification and Scene Analysis, New York: Wiley, 1973.
Friedland, N.S. and Rosenfeld, A., Compact object recognition using energy-function based optimization, IEEE Trans. Pattern Anal. Mach. Intell., 1992, vol. 14, pp. 770–777.
Geman, S. and Geman, D., Stochastic relaxation Gibbs distributions and the Bayesian restoration of the image, IEEE Trans. Pattern Anal. Mach. Intell., 1984, vol. 6, no. 6, pp. 721–741.
Haralick, R.M., Joo, H., Lee, C., Zhuang, X., Vaidya, V., and Kim, M., Pose estimation from corresponding point data, IEEE Trans. Sys., Man Cybern., 1989, vol. 19, pp. 1426–1446.
Herault, L. and Horaud, R., Figure-ground discrimination: a combinatorial optimization approach, IEEE Trans. Pattern Anal. Mach. Intell., 1993, vol. 15, pp. 899–914.
Hung, Y.P., Cooper, D.B., and Cernuschi-Frias, B., Asymtotic Bayesian surface estimation using an image sequence, Int. J. Comp. Vis., 1991, vol. 6, no. 2, pp. 105–132.
Jain, A.K., Advances in mathematical models in image processing, Proc. IEEE, 1981, no. 69, pp. 502–528.
Kondratyev, K.Ya., Kozoderov, V.V., and Smokty, O.I., Remote Sensing of the Earth from Space: Atmospheric Correction, Heidelberg: Springer, 1992.
Kozoderov, V.V., Estimation of the distorting atmospheric impact in the deciphering of natural structures from space, Aerokosmicheskie issledovaniya Zemli. Obrabotka videoinformatsii s ispol’zovaniem EVM (Aerospace Investigations of the Earth. Video Data Processing on the Basis of EVM), Moscow: Nauka, 1978, pp. 24–35.
Kozoderov, V.V., Atmospheric correction of video images, Issled. Zemli Kosmosa, 1983, no. 2, pp. 65–75.
Kozoderov, V.V., Kosolapov, V.S., Sadovnichii, V.A., Timoshin, O.A., Tishchenko, A.P., Ushakova, L.A., and Ushakov, S.A., Kosmicheskoe zemlevedenie: informatsionno-matematicheskie osnovy (Cosmic Geosciences: Informational and Mathematical Bases), Sadovnichii, V.A., Ed., Moscow: MGU, 1998.
Kozoderov, V.V., Sadovnichii, V.A., Ushakova, L.A., and Ushakov, S.A., Kosmicheskoe zemlevedenie: dialog prirody i obshchestva. Ustoichivoe razvitie (Cosmic Geosciences: Dialog between Nature and Society. Sustainable Development), Sadovnichii, V.A., Ed., Moscow: MGU, 2000.
Kozoderov, V.V., Specific features of the implementation of vegetation phytomass assessment models by space observations, Issled. Zemli Kosmosa, 2006, no. 2, pp. 79–88.
Kozoderov, V.V. and Dmitriev, E.V., Aerospace sounding the soil and vegetation cover: Models, algorithmic and software support, ground-based validation, Issled. Zemli Kosmosa, 2010, no. 1, pp. 69–86.
Kozoderov, V.V. and Dmitriev, E.V., Remote sensing of soils and vegetation: Pattern recognition and forest stand structure assessment, Int. J. Remote Sens., 2011, vol. 32, no. 3, pp. 5699–5717.
Kozoderov, V.V., Kondranin, T.V., Dmitriev, E.V., Kazantsev, O.Yu., Persev, I.V., and Shcherbakov, M.V., Processing of hyperspectral aerospace sounding data, Issled. Zemli Kosmosa, 2012, no. 5, pp. 3–11.
Kozoderov, V.V., The use of optical remote sensing data for the investigation of natural and climatic processes, Klim. Priroda, 2012, vol. 2, no. 3, pp. 3–16.
Kozoderov, V.V. and Dmitriev, E.V., Remote sensing of the forest cover: An innovative approach, Les. Vestn., 2012, vol. 1, no. 84, pp. 19–33.
Kozoderov, V.V., Computational system for the processing of hyperspectral aerospace sounding data, Abstracts of the Scientific and Technical Conference “Hyperspectral Instrumentation and Technologies,” Krasnogorskii zavod im. S.A. Zvereva, 2013, pp. 101–102.
Li, S.Z., Markov Random Field Modeling in Computer Vision, New York-Berlin-Heidelberg-Tokyo: Springer, 1995.
Olsen, R.C., Garner, J., and Dyke, E.V., Terrain classification in urban wetlands with high-spatial resolution multi-spectral imagery, Proc. SPIE-Int. Soc. Opt. Eng., 2002, vol. 4881, pp. 686–691.
Poggio, T., Torre, V., and Koch, C., Computational vision and regularization theory, Nature, 1985, vol. 317, pp. 314–319.
Tan, H.L., Gelfand, S.B., and Delp, E., A cost minimization approach to edge detection using simulated annealing, IEEE Trans. Pattern Anal. Mach. Intell., 1992, vol. 14, pp. 3–18.
Tso, B. and Olsen, R.C., A contextual classification scheme based on MRF model with improved parameter estimation and multiscale fuzzy line process, Remote Sens. Environ., 2005, vol. 97, pp. 127–136.
Ullman, S., Relaxation and constraint optimization by local process, Comp. Graph. Image Proc., 1979, vol. 10, pp. 115–195.
Author information
Authors and Affiliations
Corresponding author
Additional information
Original Russian Text © V.V. Kozoderov, E.V. Dmitriev, V.P. Kamentsev, 2013, published in Issledovanie Zemli iz Kosmosa, 2013, No. 6, pp. 57–64.
Rights and permissions
About this article
Cite this article
Kozoderov, V.V., Dmitriev, E.V. & Kamentsev, V.P. System for processing of airborne images of forest ecosystems using high spectral and spatial resolution data. Izv. Atmos. Ocean. Phys. 50, 943–952 (2014). https://doi.org/10.1134/S0001433814090114
Received:
Published:
Issue Date:
DOI: https://doi.org/10.1134/S0001433814090114