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TitleRadiometric Normalization, Compositing, and Quality Control for Satellite High Resolution Image Mosaics over Large Areas
AuthorDu, Y; Cihlar, J; Latifovic, R
SourceIEEE Transactions on Geoscience and Remote Sensing (Institute of Electrical and Electronics Engineers) 39, 3, 2001 p. 623-634,
Alt SeriesEarth Sciences Sector, Contribution Series 20042960
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Mediapaper; on-line; digital
File formatpdf
AbstractAn objective normalization procedure has been developed to create image mosaics of radiometric equalization radiometric normalization for image mosaics (RNIM). The procedure employs a band-specific principal component analysis for overlap areas to achieve accurate and consistent radiometric transforms in each spectral band. It is demonstrated that the result of radiometric equalization is independent of the order of images to be mosaicked after the radiometric normalization adjustment is made. The selection of corresponding pixel pairs in the overlap area is controlled by using band-specific linear correlation coefficients, and the criteria for rejecting the cloudy and land-cover changed pixels. The final result is controlled quantitatively by employing the first and second principal components for the input data, which in turn depend on the selection of corresponding pixel pairs in the overlap area. In general, the radiometric resolution of input images can be conserved as long as gain ³ 1 and offset ³ 0 because of the stored format of the unsigned integer. The RNIM procedure accommodates these conditions. To take the best advantage of the data in the overlap areas, a pixel-based composite technique is employed in the production of the final mosaic. The selection of corresponding pixel pairs and the final result can be controlled and assessed with quantitative criteria. Therefore, this approach produces an objective, analyst-independent result and can be automated. The method has been successfully applied to six Landsat TM images of the BOREAS transect in Saskatchewan and Manitoba, Canada. Both visual inspection and quantitative tests of the final result show that the RNIM methodology is objective and robust. It is concluded that the RNIM procedure described in this paper satisfies many desirable features for an operational mosaicking of high resolution images over large areas, including no loss of information, independence of the order of compositing, minimal processing burden, and the possibility of automation.

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