Enhanced-Random-Feature-Subspace-Based Ensemble CNN for the Imbalanced Hyperspectral Image Classification

Enhanced-Random-Feature-Subspace-Based Ensemble CNN for the Imbalanced Hyperspectral Image Classification

Hyperspectral image (HSI) classification often faces the problem of multiclass imbalance, which is considered to be one of the major challenges in the field of remote sensing. In recent years, deep learning has been successfully applied to the HSI classification, a convolutional neural network (CNN)...

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Bibliographic Details
Main Authors: Qinzhe Lv, Wei Feng, Yinghui Quan, Gabriel Dauphin, Lianru Gao, Mengdao Xing
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Subjects:
Convolutional neural network (CNN)
enhanced random feature subspace (ERFS)
ensemble learning
hyperspectral image (HSI) classification
multiclass imbalance
Online Access:https://ieeexplore.ieee.org/document/9387541/

Internet

https://ieeexplore.ieee.org/document/9387541/

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