USING SNN NEURAL NETWORKS TRAINED WITH HIGH RESOLUTION DATA 2 AND APPLIED TO COPERNICUS SENTINEL-2 DATA
Keywords: Neural Networks, Sentinel-2, Self-normalizing Neural Networks, Remote Sensing
Abstract. Data from the Copernicus project are proving to be fundamental in so many areas of spatial governance, and in particular in the context of safeguarding the cultural and natural heritage. This is mainly due to their dissemination mode, which allows them to be used by a countless number of institutions and organizations. With regard to their use in order to create effective supervised-training AI (Artificial Intelligence) classifiers (useful for continuous land-use monitoring), their low resolution could be a problem in terms of accuracy or in any case during the training process. This process which could be very costly (large training sets and high-end hardware required). In this paper, a methodology is tested that creates an AI classifier from high-resolution training data, Remote Sensing Image Classification Benchmark (RSI-CB128) and using Self-Normalising Neural Networks (SNNs). The efficiency of the Neural Network is, however, measured with a test dataset constructed with Raster Sentinel-2. In other words, the aim is to assess the goodness of an AI classifier on Sentinel-2 images built from Rasters generated by high-resolution sensors. Therefore, there seem to be clear advantages over the classification methodologies in use today.