Radiometric normalization, compositing, and quality control for satellite high resolution image mosaics over large areas

An objective normalization procedure has been developed to create image mos aics of radiometric equalization radiometric normalization for image mosaic s (RNIM), The procedure employs a band-specific principal component analysi s for overlap areas to achieve accurate and consistent radiometric transfor ms in each spectral band. It is demonstrated that the result of radiometric equalization is independent of the order of images to be mosaicked after t he radiometric normalization adjustment is made. The selection of correspon ding pixel pairs in the overlap area is controlled by using band-specific l inear correlation coefficients, and the criteria for rejecting the cloudy a nd 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 tan be conserved as long as gain greater than or equal to 1 and offset greater th an or equal to 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 employ ed in the production of the final mosaic. The selection of corresponding pi xel pairs and the final result can be controlled and assessed with quantita tive criteria. Therefore, this approach produces an objective, analyst-inde pendent result and can be automated. The method has been successfully appli ed to six Landsat TM images of the BOREAS transect in Saskatchewan and Mani toba, Canada. Both visual inspection and quantitative tests of the final re sult show that the RNIM methodology is objective and robust. It is conclude d 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 comp ositing, minimal processing burden, and the possibility of automation.