Estimating the NDVI from SAR by Convolutional Neural Networks

Mazza Antonio, Gargiulo Massimiliano, Scarpa Giuseppe, Gaetano Raffaele. 2018. Estimating the NDVI from SAR by Convolutional Neural Networks. In : Proceedings of 2018 IEEE International Geoscience and Remote Sensing Symposium: Observing, Understanding And Forecasting The Dynamics Of Our Planet. IEEE. Piscataway : IEEE, 1954-1957. ISBN 978-1-5386-7150-4 International Geoscience and Remote Sensing Symposium (IGARSS 2018), Valencia, Espagne, 22 Juillet 2018/27 Juillet 2018.

Résumé : Since optical remote sensing images are useless in cloudy conditions, a possible alternative is to resort to synthetic aperture radar (SAR) images. However, many conventional techniques for Earth monitoring applications require specific spectral features which are defined only for multispectral data. For this reason, in this work we propose to estimate missing spectral features through data fusion and deep learning, exploiting both temporal and cross-sensor dependencies on Sentinel-1 and Sentinel-2 time-series. The proposed approach, validated focusing on the estimation of the normalized difference vegetation index (NDVI), shows very interesting results with a large performance gain over the linear regression approach according to several accuracy indicators.

Auteurs et affiliations

• Mazza Antonio, University of Naples Federico II (ITA)
• Gargiulo Massimiliano, University of Naples Federico II (ITA)
• Scarpa Giuseppe, CIRAD-ES-UMR TETIS (FRA)
• Gaetano Raffaele, CIRAD-ES-UMR TETIS (FRA)

Source : Cirad-Agritrop (https://agritrop.cirad.fr/592817/)

Métriques

[ Page générée et mise en cache le 2024-12-22 ]