Pixel-Level Image Fusion using Kalman Algorithm

Data fusion aims at synergistic use of information and knowledge from different sources to aid in the overall understanding of a phenomenon. In the domain of remote sensing, where images are acquired by multiple sources or by the same source in multiple acquisition contexts, the data made available by different sources are complementary to each other, proper fusion of the data can bring better and consistent interpretation of the scene. The paper presents application of Kalman filter at pixel-level fusion. The input data collected from Ozone Monitoring Instrument (OMI) on NASA’s Aura satellite is subjected to the proposed algorithm. The performance of the algorithm is evaluated by few well-known image quality metrics.