TY - JOUR
AU - 
AB - remote sensing Article Land Cover Maps Production with High Resolution Satellite Image Time Series and Convolutional Neural Networks: Adaptations and Limits for Operational Systems 1 2 3,4, 4 3 Andrei Stoian , Vincent Poulain , Jordi Inglada * , Victor Poughon and Dawa Derksen Thales/SIX/ThereSiS, 91477 Palaiseau, France Thales Services, 31555 Toulouse, France Centre d’Etudes Spatiales de la BIOsphere (CESBIO), Université de Toulouse, CNES/CNRS/IRD/UPS/INRA, 31555 Toulouse, France Centre National d’Etudes Spatiales (CNES), 31555 Toulouse, France * Correspondence: jordi.inglada@cesbio.eu Received: 25 June 2019; Accepted: 14 August 2019; Published: 23 August 2019 Abstract: The Sentinel-2 satellite mission offers high resolution multispectral time-series image data, enabling the production of detailed land cover maps globally. When mapping large territories, the trade-off between processing time and result quality is a central design decision. Currently, this machine learning task is usually performed using pixel-wise classification methods. However, the radical shift of the computer vision field away from hand-engineered image features and towards more automation by representation learning comes with many promises, including higher quality results and less engineering effort. In particular, convolutional neural networks learn features which take into account the context of the pixels and, therefore, a better representation of the data can be 
TI - Land Cover Maps Production with High Resolution Satellite Image Time Series and Convolutional Neural Networks: Adaptations and Limits for Operational Systems
JF - Remote Sensing
DO - 10.3390/rs11171986
DA - 2019-08-23
UR - https://www.deepdyve.com/lp/unpaywall/land-cover-maps-production-with-high-resolution-satellite-image-time-H5gmrF5drE
DP - DeepDyve
ER -