Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2011, Vol. 8, No. 1, pp. 90-98
Problems of hyperspectral remote sensing imagery classification
T.V Kondranin
1, V.V. Kozoderov
2, O.Yu. Kazantsev
3, V.I. Bobylev
3, E.V. Dmitriev
4, V.D. Egorov
5, V.P. Kamentsev
6, V.V. Borzyak
21 Moscow Institute for Physics and Technology (State University)
2 M.V.Lomonosov Moscow State University
3 NPO "Lepton" Moscow Institute for Physics and Technology (State University)
4 Institute of Numerical Mathematics of Russian Academy of Sciences Moscow Institute for Physics and Technology (State University)
5 Institute of Numerical Mathematics of Russian Academy of Sciences
6 Technological Park of Tver State University
Opportunities are studied of remote sensing hyperspectral data to solve classification problems of various types of forested vegetation. Main stages are shown of the classificators (computational procedures) elaboration to process hyperspectral images. Examples are revealed of the software tool realization that enables to draw up contours of separate pixels on the spatial distribution of the RGB-synthesized images. Characteristic features are demonstrated of initial spectra and their standard deviations within the selected contours to recognize three classes of forested vegetation (spruce, pine, birth) on a particular test area.
Keywords: hyperspectral remote sensing imagery, data processing, forested vegetation classification
Full textReferences:
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