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Analysis of Artifacts in Subpixel Remote Sensing Image Registration

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4 Author(s)
Inglada, J. ; Image Anal. Office, Centre Nat. d''Etudes Spatiales, Toulouse ; Muron, V. ; Pichard, D. ; Feuvrier, T.

Subpixel accuracy image registration is needed for applications such as digital elevation model extraction, change detection, pan-sharpening, and data fusion. In order to achieve this accuracy, the deformation between the two images to be registered is usually modeled by a displacement vector field which can be estimated by measuring rigid local shifts for each pixel in the image. In order to measure subpixel shifts, one uses image resampling. Sampling theory says that, if a continuous signal has been sampled according to the Nyquist criterion, a perfect continuous reconstruction can be obtained from the sampled version. Therefore, a shifted version of a sampled signal can be obtained by interpolation and resampling with a shifted origin. Since only a sampled version of the shifted signal is needed, the reconstruction needs only to be performed for the new positions of the samples, so the whole procedure comes to computing the value of the signal for the new sample positions. In the case of image registration, the similarity between the reference image and the shifted versions of the image to be registered is measured, assuming that the maximum of similarity determines the most likely shift. The image interpolation step is thus performed a high number of times during the similarity optimization procedure. In order to reduce the computation cost, approximate interpolations are performed. Approximate interpolators will introduce errors in the resampled image which may induce errors in the similarity measure and therefore produce errors in the estimated shifts. In this paper, it is shown that the interpolation has a smoothing effect which depends of the applied shift. This means that, in the case of noisy images, the interpolation has a denoising effect, and therefore, it increases the quality of the similarity estimation. Since this blurring is not the same for every shift, the similarity may be low for a null shift (no blurring) and higher for shifts close to half a - pixel (strong blurring). This paper presents an analysis of the behavior of the different interpolators and their effects on the similarity measures. This analysis will be done for the two similarity measures: the correlation coefficient and the mutual information. Finally, a strategy to attenuate the interpolation artifacts is proposed

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Geoscience and Remote Sensing, IEEE Transactions on  (Volume:45 ,  Issue: 1 )