A dual-branch multi-feature deep fusion network framework for hyperspectral image classification

A dual-branch multi-feature deep fusion network framework for hyperspectral image classification

AbstractRecent research has proven that deep learning models are effective at mining the rich spectral-spatial information of hyperspectral images and attaining high classification performance. In this study, an end-to-end two-branch multi-feature deep fusion network framework (MFDFN) is proposed, c...

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Bibliographic Details
Main Authors: Linfeng Liu, Chengcai Zhang, Weiran Luo
Format: Article
Language:English
Published: Taylor & Francis Group 2022-12-01
Series:Geocarto International
Subjects:
Hyperspectral image classification
double branch network structure
superpixel segmentation
convolutional neural network (CNN)
attention mechanism
residual network
Online Access:https://www.tandfonline.com/doi/10.1080/10106049.2022.2143910

Internet

https://www.tandfonline.com/doi/10.1080/10106049.2022.2143910

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