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Model based view at multi-resolution image fusion methods and quality assessment measures

Palubinskas, Gintautas (2016) Model based view at multi-resolution image fusion methods and quality assessment measures. International Journal of Image and Data Fusion, 7 (3), Seiten 203-218. Taylor & Francis. doi: 10.1080/19479832.2016.1180326. ISSN 1947-9832.

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Offizielle URL: http://www.tandfonline.com/doi/abs/10.1080/19479832.2016.1180326

Kurzfassung

We propose to look at multi-resolution image fusion or pan-sharpening task from a model based perspective. Explicit definition of all models or assumptions used in the derivation of a fusion method allows us to understand the rationale or properties of existing methods and shows a way for a proper usage or proposal/selection of new methods better satisfying the needs of a particular application. Earlier mentioned property ‘better’ should be measurable quantitatively, e.g. by means of so-called quality measures. The difficulty of a quality assessment task in multi-resolution image fusion is that a reference image is missing. Existing measures or so-called protocols are still not satisfactory because quite often the rationale or assumptions are not valid or not fulfilled. From a model based view it follows naturally that a quality assessment measure can be defined as a combination of error model residuals using common or general models assumed in fusion methods. It is shown that most existing methods based on a spectral transformation or filtering are model based methods. Unfortunately, it was found out that they are based additionally on a pure pixels assumption. As they are applied for mixed pixels too that can lead to wrong fusion results. Model based view analysis shows which methods respect models assumed and thus can help to select methods which deliver correct or physically justified fusion results.

elib-URL des Eintrags:https://elib.dlr.de/104383/
Dokumentart:Zeitschriftenbeitrag
Titel:Model based view at multi-resolution image fusion methods and quality assessment measures
Autoren:
AutorenInstitution oder E-Mail-AdresseAutoren-ORCID-iDORCID Put Code
Palubinskas, GintautasGintautas.Palubinskas (at) dlr.dehttps://orcid.org/0000-0001-7322-7917NICHT SPEZIFIZIERT
Datum:2016
Erschienen in:International Journal of Image and Data Fusion
Referierte Publikation:Ja
Open Access:Nein
Gold Open Access:Nein
In SCOPUS:Ja
In ISI Web of Science:Ja
Band:7
DOI:10.1080/19479832.2016.1180326
Seitenbereich:Seiten 203-218
Herausgeber:
HerausgeberInstitution und/oder E-Mail-Adresse der HerausgeberHerausgeber-ORCID-iDORCID Put Code
Zhang, JixianChinese Academy of Surveying and Mapping, ChinaNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Verlag:Taylor & Francis
ISSN:1947-9832
Status:veröffentlicht
Stichwörter:remote sensing; image processing; multi-resolution image fusion; pan-sharpening; quality assessment; model based
HGF - Forschungsbereich:Luftfahrt, Raumfahrt und Verkehr
HGF - Programm:Raumfahrt
HGF - Programmthema:Erdbeobachtung
DLR - Schwerpunkt:Raumfahrt
DLR - Forschungsgebiet:R EO - Erdbeobachtung
DLR - Teilgebiet (Projekt, Vorhaben):R - Vorhaben hochauflösende Fernerkundungsverfahren (alt)
Standort: Oberpfaffenhofen
Institute & Einrichtungen:Institut für Methodik der Fernerkundung > Photogrammetrie und Bildanalyse
Hinterlegt von: Palubinskas, Dr.math. Gintautas
Hinterlegt am:26 Mai 2016 09:26
Letzte Änderung:20 Jun 2024 11:31

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