In: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing IEEE 2019.
Abstract
In this paper, we address the challenge of land use and land cover classification using remote sensing satellite images. For this challenging task, we use the openly and freely accessible Sentinel-2 satellite images provided within the scope of the Earth observation program Copernicus. The key contributions are as follows. We present a novel dataset based on satellite images covering 13 different spectral bands and consisting of 10 classes with in total 27,000 labeled images. We evaluate state-of-the-art deep Convolutional Neural Network (CNNs) on this novel dataset with its different spectral bands. We also evaluate deep CNNs on existing remote sensing datasets and compare the obtained results. With the proposed novel dataset, we achieved an overall classification accuracy of 98.57%. The classification system resulting from the proposed research opens a gate towards a number of Earth observation applications. We demonstrate how the classification system can be used for detecting land use or land cover changes and how it can assist in improving geographical maps.
@article{pub10161,
author = {
Helber, Patrick
and
Bischke, Benjamin
and
Dengel, Andreas
and
Borth, Damian
},
title = {Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification},
year = {2019},
journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
publisher = {IEEE}
}
German Research Center for Artificial Intelligence Deutsches Forschungszentrum für Künstliche Intelligenz
