  Imputation of missing data in large regions of satellite imagery is necessary
when the acquired image has been damaged by shadows due to clouds, or
information gaps produced by sensor failure.
  The general approach for imputation of missing data, that could not be
considered missed at random, suggests the use of other available data. Previous
work, like local linear histogram matching, take advantage of a co-registered
older image obtained by the same sensor, yielding good results in filling
homogeneous regions, but poor results if the scenes being combined have radical
differences in target radiance due, for example, to the presence of sun glint
or snow.
  This study proposes three different alternatives for filling the data gaps.
The first two involves merging radiometric information from a lower resolution
image acquired at the same time, in the Fourier domain (Method A), and using
linear regression (Method B). The third method consider segmentation as the
main target of processing, and propose a method to fill the gaps in the map of
classes, avoiding direct imputation (Method C).
  All the methods were compared by means of a large simulation study,
evaluating performance with a multivariate response vector with four measures:
Q, RMSE, Kappa and Overall Accuracy coefficients. Difference in performance
were tested with a MANOVA mixed model design with two main effects, imputation
method and type of lower resolution extra data, and a blocking third factor
with a nested sub-factor, introduced by the real Landsat image and the
sub-images that were used. Method B proved to be the best for all criteria.
