Analysis model based recovery of remote sensing data

In the past decade, the synthesis-based methods have drawn people´s attention more and more in the sparse representation community. The synthesis model decomposes the data into a combination of a few atoms of the overcomplete dictionary. However, the dual analysis-based methods have not been studied deeply. The analysis model results in a sparse outcome by multiplying an analysis dictionary. This work proposes an analysis-based recovery of the missing information of remote sensing data, by extracting supplementary information from another term of data at a different period. Our method is verified by the qualitative and quantitative assessments in the experiments.