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dc.contributor.authorWang, C
dc.contributor.authorLiu, Y
dc.contributor.authorBai, X
dc.contributor.authorTang, W
dc.contributor.authorLei, P
dc.contributor.authorZhou, J
dc.date.accessioned2018-03-06T06:01:01Z
dc.date.available2018-03-06T06:01:01Z
dc.date.issued2017
dc.identifier.issn0302-9743
dc.identifier.doi10.1007/978-3-319-71598-8_33
dc.identifier.urihttp://hdl.handle.net/10072/370475
dc.description.abstractHyperspectral image is very useful for many computer vision tasks, however it is often difficult to obtain high-resolution hyperspectral images using existing hyperspectral imaging techniques. In this paper, we propose a deep residual convolutional neural network to increase the spatial resolution of hyperspectral image. Our network consists of 18 convolution layers and requires only one low-resolution hyperspectral image as input. The super-resolution is achieved by minimizing the difference between the estimated image and the ground truth high resolution image. Besides the mean square error between these two images, we introduce a loss function which calculates the angle between the estimated spectrum vector and the ground truth one to maintain the correctness of spectral reconstruction. In experiments on two public datasets we show that the proposed network delivers improved hyperspectral super-resolution result than several state-of-the-art methods.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherSpringer
dc.relation.ispartofpagefrom370
dc.relation.ispartofpageto380
dc.relation.ispartofjournalLecture Notes in Computer Science
dc.relation.ispartofvolume10668
dc.subject.fieldofresearchInformation and Computing Sciences not elsewhere classified
dc.subject.fieldofresearchcode089999
dc.titleDeep Residual Convolutional Neural Network for Hyperspectral Image Super-Resolution
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
dc.description.versionAccepted Manuscript (AM)
gro.rights.copyright© 2017 Springer International Publishing AG. This is an electronic version of an article published in Lecture Notes In Computer Science (LNCS), volume 10668, pp 370-380, 2017. Lecture Notes In Computer Science (LNCS) is available online at: http://link.springer.com// with the open URL of your article.
gro.hasfulltextFull Text
gro.griffith.authorZhou, Jun
gro.griffith.authorWang, Chen


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