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A Novel Spatial-Spectral Pyramid Network for Hyperspectral Image Classification

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成果类型:
期刊论文
作者:
Zhou, Junbo;Zeng, Shan;Gao, Guoqiang;Chen, Yulong;Tang, Yuanyan
通讯作者:
Zeng, S
作者机构:
[Zhou, Junbo; Zeng, Shan; Chen, Yulong] Wuhan Polytech Univ, Coll Math & Comp Sci, Wuhan 430024, Peoples R China.
[Gao, Guoqiang] Southwest Jiaotong Univ, Sch Elect Engn, Chengdu 611756, Peoples R China.
[Tang, Yuanyan] Univ Macau, Fac Sci & Technol, Macau 999078, Peoples R China.
通讯机构:
[Zeng, S ] W
Wuhan Polytech Univ, Coll Math & Comp Sci, Wuhan 430024, Peoples R China.
语种:
英文
关键词:
3-D convolutional neural network (3D CNN);feature pyramid structure;hyperspectral image (HSI) classification;multiscale convolutional extraction;multiscale interfusion;spatial-spectral pyramid network (SSPN)
期刊:
IEEE Transactions on Geoscience and Remote Sensing
ISSN:
0196-2892
年:
2023
卷:
61
页码:
1-14
基金类别:
Hubei’s Key Project of Research and Development Program (Grant Number: 2023BBB046) Excellent Young and Middle-Aged Scientific and Technological Innovation Teams in Colleges and Universities of Hubei Province (Grant Number: T2021009) 10.13039/501100001809-NSFC-Civil Aviation Administration of China (CAAC) (Grant Number: U1833119)
机构署名:
本校为第一且通讯机构
院系归属:
数学与计算机学院
医学与健康学院
摘要:
As the research on deep learning methods gradually progresses, more and more classification models are applied in the classification of hyperspectral image (HSI). High-dimensional and low-resolution characteristics of HSI, however, make it difficult for conventional models to process its data effectively. In this article, a novel HSI classification model, namely, spatial–spectral pyramid network (SSPN), is designed by combining a 3-D convolutional neural network (3D CNN) with feature pyramid structure. SSPN taking advantage of 3-D convolution ...

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