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TitleRadiometric Normalization of Multi-temporal High Resolution Satellite Images with Quality Control for Land Cover Change Detection
DownloadDownloads (Preprint)
LicencePlease note the adoption of the Open Government Licence - Canada supersedes any previous licences.
AuthorDu, Y; Teillet, P M; Cihlar, J
SourceRemote Sensing of Environment vol. 82, issue 1, 2002 p. 123-134,
Alt SeriesEarth Sciences Sector, Contribution Series 20043012
PublisherElsevier BV
Mediapaper; on-line; digital
File formatpdf
Subjectsremote sensing; mapping techniques; statistical methods; statistical analysis; Thematic Mapper (TM); Principal Component Analysis (PCA); Pseudo-Invariant Features (PIFs)
Illustrationsgraphs; satellite images
AbstractThe radiometric normalization of multi-temporal satellite optical images of the same terrain is necessary for land cover change detection e.g. relative differences. In previous studies, ground reference data or pseudo invariant features (PIFs) were used in the radiometric rectification of multi-temporal images. Ground reference data are costly and difficult to acquire for most satellite remotely sensed images and the selection of PIFs is subjective generally. In addition, previous research has been focused mainly on radiometric normalization between two images acquired on different dates. The problem of conservation of radiometric resolution in the case of radiometric normalization between more than two images has not been addressed. This paper reports on a new procedure for radiometric normalization between multi-temporal images of the same area. The selection of PIFs is done statistically. With quality control, principal component analysis is used to find linear relationships between multi-temporal images of the same area. The satellite images are normalized radiometrically to a common scale tied to the reference radiometric levels. The procedure ensures the conservation of radiometric resolution for the multi-temporal images involved. The new procedure is applied to three Landsat-5 Thematic Mapper images from three different years (August 1986, 1987 and 1991) and of the same area. Quality control measures show that the error in radiometric consistency between the multi-temporal images is reduced effectively. The Normalized Difference Vegetation Index (NDVI) is calculated using the radiometrically normalized multi-temporal imagery and assessed in the context of land cover change analysis.

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