Proceedings of the 27th annual conference on Computer graphics and interactive techniques
Remote Sensing Digital Image Analysis: An Introduction
Remote Sensing Digital Image Analysis: An Introduction
The em algorithm for kernel matrix completion with auxiliary data
The Journal of Machine Learning Research
Gaps-fill of SLC-off Landsat ETM+ satellite image using a geostatistical approach
International Journal of Remote Sensing
A new image segmentation algorithm with applications to image inpainting
Computational Statistics & Data Analysis
A comprehensive framework for image inpainting
IEEE Transactions on Image Processing - Special section on distributed camera networks: sensing, processing, communication, and implementation
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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, which could not be considered missed at random, suggests the use of other available data. Previous work, like local linear histogram matching, takes 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 considers segmentation as the main target of processing, and proposes 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. Differences 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.