A novel hybrid method for synthesising missing pixels in remote sensing imagery
Missing pixels are an important challenge in remote sensing imagery because they negatively affect a number of applications. A large number of methods have been developed to interpolate missing pixel images in remote sensing. These methods are classified into four groups: deterministic, geostatistics, auxiliary image-based, and hybrid missing pixel interpolation methods. However, each method has limitations which reduce their usefulness. Interpolating missing pixel with high accuracy in
... accuracy in heterogeneous areas that exposed to irregular environmental changes is still a common gap between these methods. This project aims to develop a novel hybrid missing pixel interpolation method that is able to predict missing pixels with high accuracy in all kind of environment and changes. This thesis contributes to this task in three ways. First, it developed datasets that cover homogeneous and heterogeneous datasets with and without irregular changes. Second, it introduced a method that combined geostatistics and multi-temporal linear relations between pixels. In this combination, the fraction of change for each pixel is calculated, and then it is used to predict the missing pixel. This approach worked well when there are irregular environmental changes. Third, the thesis presented a new way for improving the interpolating performance in all kinds of the environment and changes ensuring that the interpolation method only predicts the missing pixel from its environmental class.