The point spread function (PSF) is ubiquitous in remote sensing. This paper investigated the effect of the PSF on the downscaling of continua. Geostatistical approaches were adopted to incorporate explicitly, and reduce the influence of, the PSF effect in downscaling. Two general cases were considered: univariate and multivariate. In the univariate case, the input coarse spatial resolution image is the only image available for downscaling. Area-to-point kriging was demonstrated to be a suitable solution in this case. For the multivariate case, a finer spatial resolution image (or images) observed under different conditions (e.g., at a different wavelength) is available as auxiliary data for downscaling. Area-to-point regression kriging was shown to be a suitable solution for this case. Moreover, a new solution was developed for estimating the PSF in image scale transformation. The experiments show that the PSF effect influences downscaling greatly and that downscaling can be enhanced obviously by considering the PSF effect through the geostatistical approaches and the PSF estimation solution proposed.

点扩展函数（PSF）在遥感中无处不在。本文研究了PSF对连续性缩减的影响。采用地统计方法可以明确地合并PSF效应并降低其影响。考虑了两种一般情况：单变量和多变量。在单变量情况下，输入的粗略空间分辨率图像是唯一可用于按比例缩小的图像。在这种情况下，证明了点对点克里金法是合适的解决方案。对于多元情况，在不同条件下（例如，在不同波长下）观察到的更精细的空间分辨率图像（或多个图像）可用作缩小比例的辅助数据。事实证明，点对点回归克里金法是一种合适的解决方案。此外，开发了一种新的解决方案，用于估计图像比例转换中的PSF。实验表明，通过地统计方法和提出的PSF估计解决方案，考虑PSF效应，PSF效应对降级有很大影响，并且降尺度可以明显增强。