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Please use this identifier to cite or link to this item:
http://bura.brunel.ac.uk/handle/2438/19700Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Jiang, R | - |
| dc.contributor.author | Li, X | - |
| dc.contributor.author | Gao, A | - |
| dc.contributor.author | Li, L | - |
| dc.contributor.author | Meng, H | - |
| dc.contributor.author | Yue, S | - |
| dc.contributor.author | Zhang, L | - |
| dc.date.accessioned | 2019-11-27T12:18:20Z | - |
| dc.date.available | 2019-11-27T12:18:20Z | - |
| dc.date.issued | 2019-11-14 | - |
| dc.identifier.citation | Jiang, R. et al. (2019) 'Learning Spectral and Spatial Features Based on Generative Adversarial Network for Hyperspectral Image Super-Resolution', IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, 28 July-02 August, pp. 1 - 4. doi: 10.1109/igarss.2019.8900228. | en_US |
| dc.identifier.isbn | 978-1-5386-9154-0 (ebk) | - |
| dc.identifier.isbn | 978-1-5386-9155-7 (PoD) | - |
| dc.identifier.issn | 2153-6996 | - |
| dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/19700 | - |
| dc.format.extent | 1 - 4 | - |
| dc.format.medium | Print-Electronic | - |
| dc.language.iso | en | en_US |
| dc.publisher | IEEE | en_US |
| dc.rights | Copyright © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | - |
| dc.rights.uri | https://journals.ieeeauthorcenter.ieee.org/become-an-ieee-journal-author/publishing-ethics/guidelines-and-policies/post-publication-policies/ | - |
| dc.source | IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium | - |
| dc.source | IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium | - |
| dc.subject | hyperspectral images | en_US |
| dc.subject | super-resolution | en_US |
| dc.subject | generative adversarial network | en_US |
| dc.subject | residual network | en_US |
| dc.title | Learning Spectral and Spatial Features Based on Generative Adversarial Network for Hyperspectral Image Super-Resolution | en_US |
| dc.identifier.doi | https://doi.org/10.1109/igarss.2019.8900228 | - |
| dc.relation.isPartOf | IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium | - |
| pubs.finish-date | 2019-08-02 | - |
| pubs.finish-date | 2019-08-02 | - |
| pubs.publication-status | Published | - |
| pubs.start-date | 2019-07-28 | - |
| pubs.start-date | 2019-07-28 | - |
| dc.identifier.eissn | 2153-7003 | - |
| dc.rights.holder | IEEE | - |
| Appears in Collections: | Dept of Electronic and Electrical Engineering Research Papers | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| FullText.pdf | Copyright © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | 866.75 kB | Adobe PDF | View/Open |
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