Improved technique for retrieval of forest parameters from hyperspectral remote sensing dataстатья
Статья опубликована в высокорейтинговом журнале
Информация о цитировании статьи получена из
Web of Science
Статья опубликована в журнале из списка Web of Science и/или Scopus
Дата последнего поиска статьи во внешних источниках: 16 февраля 2016 г.
Аннотация: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.