Hyperspectral Image Classification Based on Two-Branch Feature Fusion Network

Hyperspectral Image Classification Based on Two-Branch Feature Fusion Network

Effective discriminative spectral-spatial feature representation is crucial for hyperspectral image classification (HSIC). Some current methods typically extract spectral and spatial information directly from spectral-spatial 3D patches, without considering the correlation between features, resultin...

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Bibliographic Details
Main Authors: Qiongdan Huang, Liang Li, Mengyang Zhao, Jiapeng Wang, Shilin Kang
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Hyperspectral image classification (HSIC)
bidirectional long short-term memory (Bi-LSTM)
distance similarity metrics
multiscale convolutional
Online Access:https://ieeexplore.ieee.org/document/10972034/
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https://ieeexplore.ieee.org/document/10972034/

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