Deep convolutional neural networks for land-cover classification with Sentinel-2 images

Currently, analyzing satellite images requires an unsustainable amount of manual labor. Semiautomatic solutions for land-cover classification of satellite images entail the incorporation of expert knowledge. To increase the scalability of the built solutions, methods that automate image processing and analysis pipelines are required. Recently, deep learning (DL) models have been applied to challenging vision problems with great success. We expect that the use of DL models will soon outperform shallow networks and other classification algorithms, as recently achieved in multiple domains. Here, we consider the task of land-cover classification of satellite images. This seems particularly appropriate for deep classifiers due to the combined high dimensionality of the data with the presence of compositional dependencies between pixels, which can be used to characterize a particular class. We develop a pipeline for analyzing satellite images using a deep convolutional neural network for practical applications. We present its successful application for land-cover classification, where it achieves 86% classification accuracy on unseen raw images.