Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/93546
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Land Surveying and Geo-Informatics | en_US |
dc.creator | Wang, Q | en_US |
dc.creator | Shi, W | en_US |
dc.creator | Atkinson, PM | en_US |
dc.date.accessioned | 2022-07-08T01:03:02Z | - |
dc.date.available | 2022-07-08T01:03:02Z | - |
dc.identifier.issn | 0196-2892 | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/93546 | - |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers | en_US |
dc.rights | This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0/ | en_US |
dc.rights | The following publication Wang, Q., Shi, W., & Atkinson, P. M. (2019). Information loss-guided multi-resolution image fusion. IEEE Transactions on Geoscience and Remote Sensing, 58(1), 45-57 is available at https://doi.org/10.1109/TGRS.2019.2930764 | en_US |
dc.subject | Downscaling | en_US |
dc.subject | Geographically weighted regression (GWR) | en_US |
dc.subject | Geostatistics | en_US |
dc.subject | Image fusion | en_US |
dc.subject | Information loss (IL) | en_US |
dc.title | Information loss-guided multi-resolution image fusion | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 45 | en_US |
dc.identifier.epage | 57 | en_US |
dc.identifier.volume | 58 | en_US |
dc.identifier.issue | 1 | en_US |
dc.identifier.doi | 10.1109/TGRS.2019.2930764 | en_US |
dcterms.abstract | Spatial downscaling is an ill-posed, inverse problem, and information loss (IL) inevitably exists in the predictions produced by any downscaling technique. The recently popularized area-to-point kriging (ATPK)-based downscaling approach can account for the size of support and the point spread function (PSF) of the sensor, and moreover, it has the appealing advantage of the perfect coherence property. In this article, based on the advantages of ATPK and the conceptualization of IL, an IL-guided image fusion (ILGIF) approach is proposed. ILGIF uses the fine spatial resolution images acquired in other wavelengths to predict the IL in ATPK predictions based on the geographically weighted regression (GWR) model, which accounts for the spatial variation in land cover. ILGIF inherits all the advantages of ATPK, and its prediction has perfect coherence with the original coarse spatial resolution data which can be demonstrated mathematically. ILGIF was validated using two data sets and was shown in each case to predict downscaled images more accurately than the compared benchmark methods. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | IEEE transactions on geoscience and remote sensing, Jan. 2020, v. 58, no. 1, p. 45-57 | en_US |
dcterms.isPartOf | IEEE transactions on geoscience and remote sensing | en_US |
dcterms.issued | 2020-01 | - |
dc.identifier.scopus | 2-s2.0-85077954952 | - |
dc.identifier.eissn | 1558-0644 | en_US |
dc.description.validate | 202207 bcfc | en_US |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | LSGI-0134 | - |
dc.description.fundingSource | RGC | en_US |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | National Natural Science Foundation of China; Tongji University | en_US |
dc.description.pubStatus | Published | en_US |
dc.identifier.OPUS | 15714350 | - |
Appears in Collections: | Journal/Magazine Article |
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08842599.pdf | 14.28 MB | Adobe PDF | View/Open |
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