A Simplified 2D-3D CNN Architecture for Hyperspectral Image Classification Based on Spatial–Spectral Fusion

A Simplified 2D-3D CNN Architecture for Hyperspectral Image Classification Based on Spatial–Spectral Fusion

Convolutional neural networks (CNN) have led to a successful breakthrough for hyperspectral image classification (HSIC). Due to the intrinsic spatial-spectral specificities of a hyperspectral cube, feature extraction with 3-D convolution operation is a straightforward way for HSIC. However, the over...

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Bibliographic Details
Main Authors: Chunyan Yu, Rui Han, Meiping Song, Caiyu Liu, Chein-I Chang
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Subjects:
Convolution
convolutional neural networks (CNN)
feature extraction
hyperspectral image classification (HSIC)
Online Access:https://ieeexplore.ieee.org/document/9078778/

Internet

https://ieeexplore.ieee.org/document/9078778/

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