Open AccessImproved technique for retrieval of forest parameters from hyperspectral remote sensing data
Author(s) -
В. В. Козодеров,
Е. В. Дмитриев,
Anton Sokolov
Publication year - 2015
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.23.0a1342
Subject(s) - hyperspectral imaging , remote sensing , land cover , computer science , multispectral image , image resolution , canopy , environmental science , artificial intelligence , geography , land use , ecology , archaeology , biology
This paper describes an approach of machine-learning pattern recognition procedures for the land surface objects using their spectral and textural features on remotely sensed hyperspectral images together with the biological parameters retrieval for the recognized classes of forests. Modified Bayesian classifier is used to improve the related procedures in spatial and spectral domains. Direct and inverse problems of atmospheric optics are solved based on modeling results of the projective cover and density of the forest canopy for the selected classes of forests of different species and ages. Applying the proposed techniques to process images of high spectral and spatial resolution, we have detected object classes including forests within their contours on a particular image and can retrieve the phytomass amount of leaves/needles as well as the relevant total biomass amount for the forest canopy.