Iterative Random Training Sampling Spectral Spatial Classification for Hyperspectral Images

Iterative Random Training Sampling Spectral Spatial Classification for Hyperspectral Images

Hyperspectral image classification (HSIC) has generated considerable interests over the past years. However, one of challenging issues arising in HSIC is inconsistent classification, which is mainly caused by random training sampling (RTS) of selecting training data. This is because a different set...

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Bibliographic Details
Main Authors: Chein-I Chang, Kenneth Yeonkong Ma, Chia-Chen Liang, Yi-Mei Kuo, Shuhan Chen, Shengwei Zhong
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Subjects:
Hyperspectral image classification (HSIC)
iterative random training sampling (IRTS)
<named-content xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" content-type="math" xlink:type="simple"> <inline-formula> <tex-math notation="LaTeX">$K$</tex-math> </inline-formula> </named-content>-fold method
random training sampling (RTS)
spectral-spatial (SS)
Online Access:https://ieeexplore.ieee.org/document/9137652/

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https://ieeexplore.ieee.org/document/9137652/

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