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Conference Paper: Multi-scale-and-depth convolutional neural network for remote sensed imagery pan-sharpening

TitleMulti-scale-and-depth convolutional neural network for remote sensed imagery pan-sharpening
Authors
Wei, YancongYuan, QiangqiangMeng, XiangchaoShen, HuanfengZhang, LiangpeiNg, Michael
KeywordsConvolutional neural network
Residual Learning
Remote Sensing
Pan-sharpening
Deep learning
Issue Date2017
Citation
International Geoscience and Remote Sensing Symposium (IGARSS), 2017, v. 2017-July, p. 3413-3416 How to Cite?
DOI: http://dx.doi.org/10.1109/IGARSS.2017.8127731
Abstract© 2017 IEEE. Pan-sharpening is a fundamental and significant task in the field of remote sensed imagery fusion, which demands fusion of panchromatic and multi-spectral images with the rich information accurately preserved in both spatial and spectral domains. In this paper, to overcome the drawbacks of traditional pan-sharpening methodologies, we employed the advanced concept of deep learning to propose a Multi-Scale-and-Depth Convolutional Neural Network (MSDCNN) as an end-to-end pan-sharpening model. By the results of a large number of quantitative and visual assessments, the qualities of images fused by the proposed network have been confirmed superior to compared state-of-the-art methods.
Persistent Identifierhttp://hdl.handle.net/10722/276580

 

DC FieldValueLanguage
dc.contributor.authorWei, Yancong-
dc.contributor.authorYuan, Qiangqiang-
dc.contributor.authorMeng, Xiangchao-
dc.contributor.authorShen, Huanfeng-
dc.contributor.authorZhang, Liangpei-
dc.contributor.authorNg, Michael-
dc.date.accessioned2019-09-18T08:34:02Z-
dc.date.available2019-09-18T08:34:02Z-
dc.date.issued2017-
dc.identifier.citationInternational Geoscience and Remote Sensing Symposium (IGARSS), 2017, v. 2017-July, p. 3413-3416-
dc.identifier.urihttp://hdl.handle.net/10722/276580-
dc.description.abstract© 2017 IEEE. Pan-sharpening is a fundamental and significant task in the field of remote sensed imagery fusion, which demands fusion of panchromatic and multi-spectral images with the rich information accurately preserved in both spatial and spectral domains. In this paper, to overcome the drawbacks of traditional pan-sharpening methodologies, we employed the advanced concept of deep learning to propose a Multi-Scale-and-Depth Convolutional Neural Network (MSDCNN) as an end-to-end pan-sharpening model. By the results of a large number of quantitative and visual assessments, the qualities of images fused by the proposed network have been confirmed superior to compared state-of-the-art methods.-
dc.languageeng-
dc.relation.ispartofInternational Geoscience and Remote Sensing Symposium (IGARSS)-
dc.subjectConvolutional neural network-
dc.subjectResidual Learning-
dc.subjectRemote Sensing-
dc.subjectPan-sharpening-
dc.subjectDeep learning-
dc.titleMulti-scale-and-depth convolutional neural network for remote sensed imagery pan-sharpening-
dc.typeConference_Paper-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/IGARSS.2017.8127731-
dc.identifier.scopuseid_2-s2.0-85041863646-
dc.identifier.volume2017-July-
dc.identifier.spage3413-
dc.identifier.epage3416-

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