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Permuted KPCA and SMOTE to Guide GAN-Based Oversampling for Imbalanced HSI Classification

Lack of sufficient and balanced data is one of the major challenges in hyperspectral image classification. This problem can cause poor classification performance, especially in detecting or classifying samples of minority classes. The easiest way to overcome the problem is by resampling or creating...

Ausführliche Beschreibung

Gespeichert in:
Hauptverfasser: Tajul Miftahushudur, Bruce Grieve, Hujun Yin
Format: Artigo
Sprache:Inglês
Veröffentlicht: IEEE 2024-01-01
Schriftenreihe:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Schlagworte:
Generative adversarial network (GAN)
hyperspectral image (HSI)
imbalance classification
kernel principal component analysis (kernel PCA)
synthetic minority oversampling technique (SMOTE)
Online-Zugang:https://ieeexplore.ieee.org/document/10312814/
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