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Revisiting Graph Convolutional Networks with Mini-Batch Sampling for Hyperspectral Image Classification

Hong, Danfeng and Gao, Lianru and Wu, Xin and Yao, Jing and Zhang, Bing (2021) Revisiting Graph Convolutional Networks with Mini-Batch Sampling for Hyperspectral Image Classification. In: 11th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, WHISPERS 2021, pp. 1-4. WHISPERS 2021, 24.-26. März 2021, Amsterdam/NL. doi: 10.1109/WHISPERS52202.2021.9484014. ISBN 978-166543601-4. ISSN 2158-6276.

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Official URL: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9484014

Abstract

Graph convolutional networks (GCNs) have been success-fully and widely applied in computer vision and machinelearning fields. As a powerful tool, GCNs have recentlyreceived increasing attention in the remote sensing commu-nity, e.g., hyperspectral image (HSI) classification. However,the application ability of GCNs in identifying the materi-als via spectral signatures remains limited, since traditionalGCNs fail to extract node features for large-scale graphs ef-ficiently. Also, simultaneous consideration of all samplesin GCNs tends to obtain poor representations, possibly dueto the vanishing gradient problem. To this end, we in thispaper develop a novel mini-batch GCN (miniGCN) for HSimage classification. More importantly, miniGCN not onlycan effectively train the network via mini-batch sampling ina supervised way, but also directly infer new samples (out-of-sample) without re-training GCNs. Experiments conductedon two commonly-used HSI datasets demonstrate the superi-ority of miniGCN over other state-of-the-art network archi-tectures.

Item URL in elib:https://elib.dlr.de/144440/
Document Type:Conference or Workshop Item (Speech)
Title:Revisiting Graph Convolutional Networks with Mini-Batch Sampling for Hyperspectral Image Classification
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Hong, DanfengDanfeng.Hong (at) dlr.deUNSPECIFIED
Gao, LianruChinese Academy of SciencesUNSPECIFIED
Wu, XinBeijing Institute of TechnologyUNSPECIFIED
Yao, JingChinese Academy of SciencesUNSPECIFIED
Zhang, BingChinese Academy of SciencesUNSPECIFIED
Date:March 2021
Journal or Publication Title:11th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, WHISPERS 2021
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:No
DOI :10.1109/WHISPERS52202.2021.9484014
Page Range:pp. 1-4
ISSN:2158-6276
ISBN:978-166543601-4
Status:Published
Keywords:Classification, deep learning, graph con-volutional network, hyperspectral image, mini-batch
Event Title:WHISPERS 2021
Event Location:Amsterdam/NL
Event Type:international Conference
Event Dates:24.-26. März 2021
Organizer:IEEE
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Artificial Intelligence
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > EO Data Science
Deposited By: Haschberger, Dr.-Ing. Peter
Deposited On:08 Oct 2021 12:17
Last Modified:12 Oct 2021 10:09

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