How To Normalize Satellite Images For Deep Learning

Normalization of input data for deep learning (DL) applications is an important step that impacts network convergence and final results. In case of long-tailed satellite signals, proper normalization can be quite a challenge -- we were tired of trying to understand why the models we trained on one location didn't always translate to another location as well as we thought they should -- so we set out to explore what kind of normalization schemes are most suited for the task. Deep-learning-based automatic field delineation from satellite images is becoming an important tool in large-scale evaluations and monitoring of land cover and crop production. One of the steps in the workflow is normalization of the band values, which impacts network performance and quality of the results. The aim of this study is to investigate and quantify the effects of several normalization methods on the performance of our existing field delineation algorithm.