Testing different classification methods in airborne hyperspectral imagery processingстатья
Статья опубликована в высокорейтинговом журнале
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Статья опубликована в журнале из списка Web of Science и/или Scopus
Дата последнего поиска статьи во внешних источниках: 19 июля 2016 г.
Аннотация:To enhance the efficiency of machine-learning algorithms of
optical remote sensing imagery processing, optimization techniques are
evolved of the land surface objects pattern recognition. Different methods
of supervised classification are considered for these purposes, including the
metrical classifier operating with Euclidean distance between any points of
the multi-dimensional feature space given by registered spectra, the Knearest
neighbors classifier based on a majority vote for neighboring pixels
of the recognized objects, the Bayesian classifier of statistical decision
making, the Support Vector Machine classifier dealing with stable solutions
of the mini-max optimization problem and their different modifications. We
describe the related techniques applied for selected test regions to compare
the listed classifiers.