Determining the optimum image fusion method for better interpretation of the surface of the Earth.
Image fusion is the production of high-resolution images by combining the spatial details of a high-resolution image with the spectral features of a low-resolution one. Reports of various quality metrics to evaluate the spectral and spatial qualities of fused images have been published. However, met...
| Publicado en: | Norwegian Journal of Geography Vol. 70; no. 2; pp. 69 - 82 |
|---|---|
| Autores principales: | , |
| Formato: | Artículo |
| Publicado: |
Taylor & Francis Ltd
May2016
|
| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=118223507&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 118223507 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00291951 9DP jtl: Norwegian Journal of Geography issn: 00291951 maglogo: N pubinfo: dt: May2016 vid: 70 iid: 2 pid: 377 pub: Taylor & Francis Ltd artinfo: ui: 118223507 10.1080/00291951.2015.1126761 ppf: 69 ppct: 13 formats: tig: atl: Determining the optimum image fusion method for better interpretation of the surface of the Earth. aug: au: Yilmaz, Volkan Gungor, Oguz affil: Geomatics Department, Karadeniz Technical University, Trabzon, TR-61080, Turkey su: Image fusion Algorithm software Surface of the earth Big data Electromagnetic waves sug: subj: Image fusion Algorithm software Surface of the earth Big data Electromagnetic waves keyword: Catriona Turner classification image fusion Ivar Berthling pansharpening quality metrics spectral resolution ab: Image fusion is the production of high-resolution images by combining the spatial details of a high-resolution image with the spectral features of a low-resolution one. Reports of various quality metrics to evaluate the spectral and spatial qualities of fused images have been published. However, metrics may lead to misinterpretation due to inherent limitations in their mathematical algorithms. Hence, the use of additional assessment techniques in quality evaluation is reasonable. The purpose of the study was to compare the performances of several advanced fusion algorithms in order to help users in their choice of an appropriate fusion algorithm. Four different datasets were fused using advanced fusion algorithms, namely UNB PanSharp, Hyperspherical Color Space, High-Pass Filtering, Ehlers, Subtractive, Wavelet Single Band, Gram-Schmidt, Flexible Pixel-Based, and Criteria-Based. The spectral and spatial qualities of the fused images were evaluated using various quantitative procedures to ensure comprehensive and reliable comparison. The results showed that the Flexible Pixel-Based and High-Pass Filtering algorithms were very successful with regard to spatial quality, whereas the Flexible Pixel-Based and Criteria-Based algorithms were very successful with regard to spectral quality. The authors conclude that the Flexible Pixel-Based algorithm can be used for applications that require high spectral and spatial quality. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2016 holdings: @attributes: islocal: N |
|---|