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Reducing Shadow Effects on the Co-Registration of Aerial Image Pairs

Image registration is an important preprocessing step prior to detecting changes using multi-temporal image data, which is increasingly accomplished using automated methods. In high spatial resolution imagery, shadows represent a major source of illumination variation, which can reduce
 the performance of automated registration routines. This study evaluates the statistical relationship between shadow presence and image registration accuracy, and whether masking and normalizing shadows leads to improved automatic registration results. Eighty-eight bitemporal aerial image
 pairs were co-registered using software called Scale Invariant Features Transform (SIFT) and Random Sample Consensus (RANSAC) Alignment (SARA). Co-registration accuracy was assessed at different levels of shadow
 coverage and shadow movement within the images. The primary outcomes of this study are (1) the amount of shadow in a multi-temporal image pair is correlated with the accuracy/success of automatic co-registration; (2) masking out shadows prior to match point select does not improve the success
 of image-to-image co-registration; and (3) normalizing or brightening shadows can help match point routines find more match points and therefore improve performance of automatic co-registration. Normalizing shadows via a standard linear correction provided the most reliable co-registration
 results in image pairs containing substantial amounts of relative shadow movement, but had minimal effect for pairs with stationary shadows.