Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2018, Vol. 15, No. 3, pp. 21-28
Capabilities of remote monitoring of Earth surface objects with a hyperspectral complex in the band of 400–1700 nm
A.N. Vinogradov
1 , D.S. Demidova
1 , V.V. Egorov
2 , A.A. Ilyin
1 , A.P. Kalinin
3 , A.I. Rodionov
1 , I.D. Rodionov
4 , I.P. Rodionova
4 1 “Reagent” Research and Development Center, Moscow, Russia
2 Space Research Institute RAS, Moscow, Russia
3 Ishlinsky Institute for Problems in Mechanics RAS, Moscow, Russia
4 Semyonov Institute of Chemical Physics RAS, Moscow, Russia
Accepted: 21.12.2017
DOI: 10.21046/2070-7401-2018-15-3-21-28
The problems of aviation hyperspectral remote sensing of the Earth surface using visible and near infrared sensors have been considered. Technical characteristics of onboard hyperspectral modules of 400–1000 and 900–1700 nm bands of a high spatial and spectral resolution are provided. The aviation test carried out in the Moscow region in Summer 2016 from board of the An-2 plane (800–900 m flight height) in the afternoon is described. The methodology for processing the obtained hyperspectral cubes is based on algorithms of both controlled and non-controlled classification. RGB-images of terrains covered by forest, agricultural plants and grass and those occupied by private plots and roads have been received. Calculated images of the principle components were based on initial hyperspectral cubes received from both modules. Statistical processing of the images is limited to calculation of histograms followed by an approximation by Gaussian curves using the technique of mixture separation. The obtained approximations are used for calculation of the accuracy tables relating both to separate and aggregate usage of the two hyperspectral modules’ data. As a result, probability value for a correct classification of the scene objects using separate modules processing is estimated at 0.85 in average (excluding private plots and roads) and under aggregate processing at 0.98.
Keywords: hyperspectrometer, visual and near infrared band, natural object, principal components, classification, cluster, false alarm, matrix of accuracy
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