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A Dense Pyramidal Residual Network with a Tandem Spectral-Spatial Attention Mechanism for Hyperspectral Image Classification.

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第一作者： Yunlan,Guan

第一单位： Key Laboratory of Mine Environmental Monitoring and Improving around Poyang Lake of Ministry of Natural Resources, East China University of Technology, Nanchang 330013, China.;School of Surveying and Geoinformation Engineering, East China University of Technology, Nanchang 330013, China.;Jiangxi Key Laboratory of Watershed Ecological Process and Information, East China University of Technology, Nanchang 330013, China.

作者： Yunlan,Guan ^[1] ; Zixuan,Li ^[2] ; Nan,Wang ^[2]

作者单位： Key Laboratory of Mine Environmental Monitoring and Improving around Poyang Lake of Ministry of Natural Resources, East China University of Technology, Nanchang 330013, China.;School of Surveying and Geoinformation Engineering, East China University of Technology, Nanchang 330013, China.;Jiangxi Key Laboratory of Watershed Ecological Process and Information, East China University of Technology, Nanchang 330013, China. ^[1] School of Surveying and Geoinformation Engineering, East China University of Technology, Nanchang 330013, China. ^[2]

关键词 convolutional neural network dense pyramid residual network dilated convolution hyperspectral image classification residual unit tandem attention mechanism

DOI 10.3390/s25061858

PMID 40292975

发布时间 2025-04-30

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