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dc.contributor.authorHadjimitsis, Diofantos G.-
dc.contributor.authorClayton, Chris R I-
dc.contributor.otherΧατζημιτσής, Διόφαντος Γ.-
dc.date.accessioned2012-11-26T12:00:21Zen
dc.date.accessioned2013-05-17T10:30:56Z-
dc.date.accessioned2015-12-09T13:52:01Z-
dc.date.available2012-11-26T12:00:21Zen
dc.date.available2013-05-17T10:30:56Z-
dc.date.available2015-12-09T13:52:01Z-
dc.date.issued2007-
dc.identifier.citationProceedings of SPIE - The international society for optical engineering, 2007, vol. 6749, no. 674936en_US
dc.identifier.issn0277786X-
dc.identifier.urihttps://hdl.handle.net/20.500.14279/4499-
dc.description.abstractThe Covariance Matrix Method (CMM) uses the statistical relationship between all the selected bands of a satellite sensor simultaneously, rather than one at a time as in the regression method. It examines the set of variances and covariance between all band pairs in the image data and CMM provides an average pixel correction for a specified part of a satellite image. It is necessary to know a priori a value for the atmospheric path radiance on one spectral band. From this, CMM enables the estimation of the atmospheric path radiances in all the other bands. Dark pixels must be present in the CMM technique. Indeed, the authors suggest an improved CMM atmospheric correction algorithm. This methodology has been presented as an improved revised version of the CMM atmospheric approach. The authors provide a critical assessment of the suitability of the CMM atmospheric correction using Landsat TM image data of an area consisting low reflectance targets that have been used for several environmental monitoring applications. The proposed improved method produces retrieved surface reflectance within the range of the ground measurementsen_US
dc.formatpdfen_US
dc.language.isoenen_US
dc.rights© SPIEen_US
dc.subjectRemote sensingen_US
dc.subjectStatistical methodsen_US
dc.subjectSatellitesen_US
dc.titleThe application of the covariance matrix statistical method for removing atmospheric effects from satellite remotely sensed data intended for environmental applicationsen_US
dc.typeArticleen_US
dc.collaborationUniversity of Southamptonen_US
dc.collaborationCyprus University of Technologyen_US
dc.subject.categoryCivil Engineeringen_US
dc.journalsSubscriptionen_US
dc.reviewpeer reviewed-
dc.countryCyprusen_US
dc.countryUnited Kingdomen_US
dc.subject.fieldEngineering and Technologyen_US
dc.publicationPeer Revieweden_US
dc.identifier.doi10.1117/12.751887en_US
dc.dept.handle123456789/148en
dc.relation.issue674936en_US
dc.relation.volume6749en_US
cut.common.academicyear2007-2008en_US
item.fulltextNo Fulltext-
item.languageiso639-1en-
item.grantfulltextnone-
item.openairecristypehttp://purl.org/coar/resource_type/c_6501-
item.cerifentitytypePublications-
item.openairetypearticle-
crisitem.author.deptDepartment of Civil Engineering and Geomatics-
crisitem.author.facultyFaculty of Engineering and Technology-
crisitem.author.orcid0000-0002-2684-547X-
crisitem.author.parentorgFaculty of Engineering and Technology-
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