Support Vector Regression With Kernel Combination for Missing Data Reconstruction

Over the past few years, the reconstruction of missing data due to the presence of clouds received an important attention. Applying region-based inpainting strategies or conventional regression methods, such as support vector (SV) machine regression, may not be the optimal way. In this letter, we propose new combinations of kernel functions with which we obtain a better reconstruction. In particular, in the regression, we add to the radiometric information, i.e., the position information of the pixels in the image. For each kind of information adopted in the regression, a specific kernel is selected and adapted. Adopting this new kernel combination in a SV regression (SVR) comes out that only few SVs are needed to reconstruct a missing area. This means that we also perform a compression in the number of values needed for a good reconstruction. We illustrate the proposed approaches through some simulations on FORMOSAT-2 multitemporal images.