Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/81143
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Computing | - |
dc.creator | Li, XY | - |
dc.creator | Zhang, LF | - |
dc.creator | You, JN | - |
dc.date.accessioned | 2019-07-29T03:18:09Z | - |
dc.date.available | 2019-07-29T03:18:09Z | - |
dc.identifier.uri | http://hdl.handle.net/10397/81143 | - |
dc.language.iso | en | en_US |
dc.publisher | Molecular Diversity Preservation International | en_US |
dc.rights | © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). | en_US |
dc.rights | The following publication Li, X.; Zhang, L.; You, J. Domain Transfer Learning for Hyperspectral Image Super-Resolution. Remote Sens. 2019, 11, 694, 19 pages is available at https://dx.doi.org/10.3390/rs11060694 | en_US |
dc.subject | Hyperspectral Image (HSI) super-resolution | en_US |
dc.subject | Domain transfer learning | en_US |
dc.subject | Convolutional super-resolution network | en_US |
dc.subject | Image fusion | en_US |
dc.title | Domain transfer learning for hyperspectral image super-resolution | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 1 | - |
dc.identifier.epage | 19 | - |
dc.identifier.volume | 11 | - |
dc.identifier.issue | 6 | - |
dc.identifier.doi | 10.3390/rs11060694 | - |
dcterms.abstract | A Hyperspectral Image (HSI) contains a great number of spectral bands for each pixel; however, the spatial resolution of HSI is low. Hyperspectral image super-resolution is effective to enhance the spatial resolution while preserving the high-spectral-resolution by software techniques. Recently, the existing methods have been presented to fuse HSI and Multispectral Images (MSI) by assuming that the MSI of the same scene is required with the observed HSI, which limits the super-resolution reconstruction quality. In this paper, a new framework based on domain transfer learning for HSI super-resolution is proposed to enhance the spatial resolution of HSI by learning the knowledge from the general purpose optical images (natural scene images) and exploiting the cross-correlation between the observed low-resolution HSI and high-resolution MSI. First, the relationship between low- and high-resolution images is learned by a single convolutional super-resolution network and then is transferred to HSI by the idea of transfer learning. Second, the obtained Pre-high-resolution HSI (pre-HSI), the observed low-resolution HSI, and high-resolution MSI are simultaneously considered to estimate the endmember matrix and the abundance code for learning the spectral characteristic. Experimental results on ground-based and remote sensing datasets demonstrate that the proposed method achieves comparable performance and outperforms the existing HSI super-resolution methods. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Remote sensing, 2 Mar. 2019, v. 11, no. 6, 694, p. 1-19 | - |
dcterms.isPartOf | Remote sensing | - |
dcterms.issued | 2019 | - |
dc.identifier.isi | WOS:000465615300071 | - |
dc.identifier.eissn | 2072-4292 | - |
dc.identifier.artn | 694 | - |
dc.description.validate | 201907 bcrc | - |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | OA_Scopus/WOS | en_US |
dc.description.pubStatus | Published | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Li_Transfer_Hyperspectral_Super-resolution.pdf | 8.69 MB | Adobe PDF | View/Open |
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