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dc.contributor.authorLiu, Yao
dc.contributor.authorGao, Lianru
dc.contributor.authorXiao, Chenchao
dc.contributor.authorQu, Ying
dc.contributor.authorZheng, Ke
dc.contributor.authorMarinoni, Andrea
dc.date.accessioned2021-11-05T12:41:36Z
dc.date.available2021-11-05T12:41:36Z
dc.date.issued2020-06-01
dc.description.abstractConvolutional neural networks (CNNs) have been widely applied in hyperspectral imagery (HSI) classification. However, their classification performance might be limited by the scarcity of labeled data to be used for training and validation. In this paper, we propose a novel lightweight shuffled group convolutional neural network (abbreviated as SG-CNN) to achieve efficient training with a limited training dataset in HSI classification. SG-CNN consists of SG conv units that employ conventional and atrous convolution in different groups, followed by channel shuffle operation and shortcut connection. In this way, SG-CNNs have less trainable parameters, whilst they can still be accurately and efficiently trained with fewer labeled samples. Transfer learning between different HSI datasets is also applied on the SG-CNN to further improve the classification accuracy. To evaluate the effectiveness of SG-CNNs for HSI classification, experiments have been conducted on three public HSI datasets pretrained on HSIs from different sensors. SG-CNNs with different levels of complexity were tested, and their classification results were compared with fine-tuned ShuffleNet2, ResNeXt, and their original counterparts. The experimental results demonstrate that SG-CNNs can achieve competitive classification performance when the amount of labeled data for training is poor, as well as efficiently providing satisfying classification results.en_US
dc.identifier.citationLiu, Gao, Xiao, Qu, Zheng, Marinoni. Hyperspectral image classification based on a shuffled group convolutional neural network with transfer learning. Remote Sensing. 2020;12(11)en_US
dc.identifier.cristinIDFRIDAID 1895743
dc.identifier.doi10.3390/rs12111780
dc.identifier.issn2072-4292
dc.identifier.urihttps://hdl.handle.net/10037/22936
dc.language.isoengen_US
dc.publisherMDPIen_US
dc.relation.journalRemote Sensing
dc.relation.projectIDinfo:eu-repo/grantAgreement/RCN/SFI/237906/Norway/Centre for Integrated Remote Sensing and Forecasting for Arctic Operations/CIRFA/en_US
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2020 The Author(s)en_US
dc.subjectVDP::Technology: 500en_US
dc.subjectVDP::Teknologi: 500en_US
dc.subjectVDP::Mathematics and natural science: 400::Physics: 430en_US
dc.subjectVDP::Matematikk og Naturvitenskap: 400::Fysikk: 430en_US
dc.titleHyperspectral image classification based on a shuffled group convolutional neural network with transfer learningen_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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