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An Enhanced Spectral Fusion 3D CNN Model for Hyperspectral Image Classification

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成果类型:
期刊论文
作者:
Zhou, Junbo;Zeng, Shan;Xiao, Zuyin;Zhou, Jinbo;Li, Hao;...
通讯作者:
Shan Zeng
作者机构:
[Zhou, Junbo; Xiao, Zuyin; Zhou, Jinbo; Li, Hao; Kang, Zhen; Zeng, Shan] Wuhan Polytechn Univ, Sch Math & Comp Sci, Wuhan 430023, Peoples R China.
通讯机构:
[Shan Zeng] S
School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan 430023, China<&wdkj&>Author to whom correspondence should be addressed.
语种:
英文
关键词:
deep learning;hyperspectral image classification;attention mechanism;feature fusion;3D CNN
期刊:
Remote Sensing
ISSN:
2072-4292
年:
2022
卷:
14
期:
21
页码:
5334-
基金类别:
This research was funded by the Hubei Province Natural Science Foundation for Distinguished Young Scholars, grant No. 2020CFA063, and funded by excellent young and middle-aged scientific and technological innovation teams in colleges and universities of Hubei Province, grant No. T2021009.
机构署名:
本校为第一机构
院系归属:
数学与计算机学院
摘要:
With the continuous development of hyperspectral image technology and deep learning methods in recent years, an increasing number of hyperspectral image classification models have been proposed. However, due to the numerous spectral dimensions of hyperspectral images, most classification models suffer from issues such as breaking spectral continuity and poor learning of spectral information. In this paper, we propose a new classification model called the enhanced spectral fusion network (ESFNet), which contains two parts: an optimized multi-sca...

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