Compressed spatial–spectral feature representation for hyperspectral ground classification

Compressed spatial–spectral feature representation for hyperspectral ground classification

The difficulty of classification tasks in hyperspectral imagery (HSI) strongly depends on the representation of spectral or spatial information. Vast amounts of approaches have been proposed to deal with spectral and spatial feature extraction, respectively. However, most of the methods neglect the...

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Bibliographic Details
Main Authors: Zhou Shichao, Zhao Baojun, Tang Linbo, Wang Wenzheng
Format: Article
Language:English
Published: Wiley 2019-10-01
Series:The Journal of Engineering
Subjects:
hyperspectral imaging
feature extraction
geophysical image processing
pattern classification
image representation
learning (artificial intelligence)
image classification
multiple sources representation learning
resultant features
hsi classification
multiple network channels
optimal joint feature
fusion-model
classification accuracy
spatial–spectral-joint model
deep learning framework
hyperspectral benchmarks
compressed spatial–spectral feature representation
hyperspectral ground classification
classification tasks
hyperspectral imagery
spectral information
spatial information
spectral feature extraction
spatial feature extraction
methods neglect
inherent relationships
extreme learning machine
fusion-elm framework
Online Access:https://digital-library.theiet.org/content/journals/10.1049/joe.2019.0320

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https://digital-library.theiet.org/content/journals/10.1049/joe.2019.0320

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