Software framework for satellite spatial resolution enhancement

Remote sensing provides many crucial data today. Thankfully to the ease of access, global coverage and short revisit time intervals it became possible to retrieve global Earth’s land coverage data effortlessly. This data can provide useful information of the Earth’s land cover current state to make necessary assessments, forecasts, and other tasks that can be in handy for humanity, governments or even farmers. One of the main characteristics of image data quality is its spatial resolution. Thus, spatial resolution enhancement is a relevant topic nowadays. In this article a generalized software framework for satellite spatial resolution enhancement is presented. Due to sensitivity to the satellite data distortion, the applied method considers fusion of several low-resolution images into a single super-resolved one. The proposed framework takes into account satellite data specificity, that is given in a corresponding section. The framework was described to be capable to operate with radar and optical data. For the radar data a corresponding module, that ensures applicability of the super-resolution approach, is given. The framework was implemented using, mainly, C/C++ programming language and tested on a series of real satellite images. The result was evaluated using the modulation transfer function (MTF) approach and has shown an increasement in 135.91% for threefold scale optical images spatial resolution enhancement and 30.93% for the twofold scale radar spatial resolution enhancement. Despite the given representability of the test image set, the presented approach can be beneficial for the tasks that may have a need of the satellite data with higher spatial resolution. The paper concludes with overview of the authors implementation of the given framework and highlighting its drawbacks with suggestions for improvement.