Global Prototypical Network for Few-Shot Hyperspectral Image Classification

Global Prototypical Network for Few-Shot Hyperspectral Image Classification

This article proposes a global prototypical network (GPN) to solve the problem of hyperspectral image classification using limited supervised samples (i.e., few-shot problem). In the proposed method, a strategy of global representation learning is adopted to train a network (f<sub>&#x03B8;...

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Bibliographic Details
Main Authors: Chengye Zhang, Jun Yue, Qiming Qin
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Subjects:
Deep Learning
dense convolution
global representations
hyperspectral image classification
small number of samples
spectral–spatial attention
Online Access:https://ieeexplore.ieee.org/document/9170766/

Internet

https://ieeexplore.ieee.org/document/9170766/

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