Non uniform sampling for remote sensing images

Abstract

This paper deals with non uniform sampling of remote sensing optical images. Non uniform sampling can model defects of the uniform sampling device such as grid distortion or dead pixels. Moreover, non uniform sampling can be intentionally introduced either for its good properties with respect to aliasing or in the case of variable rate devices such as telemetry and remote control. Image reconstruction from non uniform samples requires appropriate interpolation formulas taking into account the sample locations. This paper compares cubic spline interpolation and a modified Lagrangian formula in terms of Peak Signal to Noise Ratio (PSNR) in the case of high resolution remote sensing optical images. Non uniform sampling shows a higher robustness with respect to aliasing than the classical uniform sampling. In case of non uniform sampling, the modified Lagrange reconstruction formula leads to a higher image quality than the cubic spline interpolation.