A new kernel method for hyperspectral image
feature extraction
Bin Zhao;Lianru Gao;Wenzhi Liao;Bing Zhang
【期刊名称】《地球空间信息科学学报（英文版）》 【年(卷),期】2017(020)004
【摘要】Hyperspectral image provides abundant spectral information for
remote
discrimination
of
subtle
differences
in
ground
covers.However,the increasing spectral dimensions,as well as the information redundancy,make the analysis and interpretation of hyperspectral images a challenge.Feature extraction is a very important step for hyperspectral image processing.Feature extraction methods aim at reducing the dimension of data,while preserving as much information
as
possible.Particularly,nonlinear
feature
extraction
methods (e.g.kernel minimum noise fraction (KMNF) transformation) have been reported to benefit many applications of hyperspectral remote sensing,due to their good preservation of high-order structures of the original data.However,conventional KMNF or its extensions have some limitations on noise fraction estimation during the feature extraction,and
this
leads
to
poor
performances
for
post-
applications.This paper proposes a novel nonlinear feature extraction method for hyperspectral images.Instead of estimating noise fraction by the nearest neighborhood information (within a sliding window),the
A new kernel method for hyperspectral image feature extraction
