A semi-supervised cycle-GAN neural network for hyperspectral image classification with minimum noise fraction

A semi-supervised cycle-GAN neural network for hyperspectral image classification with minimum noise fraction

Hyperspectral imaging (HSI) is a popular mode of remote sensing imaging that collects data beyond the visible spectrum. Many classification techniques have been developed in recent years, since classification is the most crucial task in hyperspectral image processing. Furthermore, extracting feature...

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Bibliographic Details
Main Authors: Tatireddy Subba Reddy, Jonnadula Harikiran
Format: Article
Language:English
Published: IM Publications Open 2022-03-01
Series:Journal of Spectral Imaging
Subjects:
feature extraction
hyperspectral image
cycle-gans
semi-supervised classification
minimum noise fraction (mnf)
aviris sensors
rolling guidance filter (rgf)
indian pines
transductive support vector machine (tsvm)
convolutional neural networks (cnn)
Online Access:https://www.impopen.com/download.php?code=I11_a2

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https://www.impopen.com/download.php?code=I11_a2

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