The performance of classifiers in the task of thematic processing of hyperspectral images
Автор: Dmitriev Egor V., Kozoderov Vladimir V.
Журнал: Журнал Сибирского федерального университета. Серия: Техника и технологии @technologies-sfu
Статья в выпуске: 7 т.9, 2016 года.
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The performance of the spectral classification methods is analyzed for the problem of hyperspectral remote sensing of soil and vegetation. The characteristic features of metric classifi ers, parametric Bayesian classifiers and multiclass support vector machines are discussed. The results of classifi cation of hyperspectral airborne images by using the specifi ed above methods and comparative analysis are demonstrated. The advantages of the use of nonlinear classifiers are shown. It is also shown, the similarity of the results of some modifications of support vector machines and Bayesian classifi cation.
Remote sensing, pattern recognition, spectral classification, hyperspectral measurements
Короткий адрес: https://sciup.org/146115129
IDR: 146115129 | DOI: 10.17516/1999-494X-2016-9-7-1001-1011
Список литературы The performance of classifiers in the task of thematic processing of hyperspectral images
- Knorn J., Rabe A., Volker C., Radeloff C.V., Kuemmerl T., Kozak J., Hostert P. Land cover mapping of large areas using chain classification of neighboring Landsat satellite images. Remote Sensing of Environment, 2009, 113(5), 957-964.
- Ellis E.C., Wang H., Xiao H., Peng K., Liu X.P., Li S.C., Ouyang H., Cheng X., Yang L.Z. Measuring long-term ecological changes in densely populated landscapes using current and historical high resolution imagery. Remote Sensing of Environment, 2006, 100(4), 457-473.
- Kozoderov V.V., Dmitriev E.V. Remote sensing of soils and vegetation: pattern recognition and forest stand structure assessment. International Journal of Remote Sensing, 2011, 32(20), 5699-5717.
- Turner W., Spector S., Gardiner N., Fladeland M., Sterling E., Steininger M. Remote sensing for biodiversity science and conservation. Trends in Ecology and Evolution, 2003, 18(6), 306-314.
- Kozoderov V.V., Kondranin T.V., Dmitriev E.V., Kamentsev V.P. A system for processing hyperspectral imagery: application to detecting forest species. International Journal of Remote Sensing, 2014, 35(15), 5926-5945.
- Vyas D., Krishnayya N.S.R., Manjunath K.R., Ray S.S., Panigrahy S. International Journal of Applied Earth Observation and Geoinformation. International Journal of Applied Earth Observation and Geoinformation, 2011, 13(2), 228-235.
- Ghosh A., Fassnacht F.E., Joshi P.K., Koch B. International Journal of Applied Earth Observation and Geoinformation. International Journal of Applied Earth Observation and Geoinformation, 2014, 26, 49-63.
- Hastie T., Tibshirani R., Friedman J. The Elements of Statistical Learning, second edition. New York: Springer, 2008. 739 p.
- Kozoderov V.V., Kondranin T.V., Dmitriev E.V., Sokolov A.A. Retrieval of forest stand attributes using optical airborne remote sensing data. Optics Express, 2014, 22(13), 15410-15423.
- Dmitriev E.V. Classification of the Forest Cover of Tver’ Region Using Hyperspectral Airborne Imagery. Izvestiya, Atmospheric and Oceanic Physics, 2014, 50(9), 929-942.
