MS<inline-formula><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula>A-Net: Multiscale Spectral&#x2013;Spatial Association Network for Hyperspectral Image Clustering

MS<inline-formula><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula>A-Net: Multiscale Spectral&#x2013;Spatial Association Network for Hyperspectral Image Clustering

Remote sensing hyperspectral cameras acquire high spectral-resolution data that reveal valuable composition information on the targets (e.g., for Earth observation and environmental applications). The intrinsic high dimensionality and the lack of sufficient numbers of labeled/training samples preven...

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Bibliographic Details
Main Authors: Kasra Rafiezadeh Shahi, Pedram Ghamisi, Behnood Rasti, Richard Gloaguen, Paul Scheunders
Format: Article
Language:English
Published: IEEE 2022-01-01
Series:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Subjects:
Convolutional autoencoder
deep learning
dilated (atrous) convolutions
hyperspectral imaging
multiscale information
remote sensing
Online Access:https://ieeexplore.ieee.org/document/9855229/

Internet

https://ieeexplore.ieee.org/document/9855229/

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