A deep learning-based approach for crop classification using dual-polarimetric C-band radar data

Publikation: KonferencebidragPosterForskningpeer review

ESA operates the Sentinel-1 satellites, which provides Synthetic Aperture Radar (SAR) data of Earth. Recorded Sentinel-1 data have shown a potential for remotely observing and monitoring local conditions on broad acre fields. Remote sensing using Sentinel-1 have the potential to provide daily updates on the current conditions in the individual fields and at the same time give an overview of the agricultural areas in the region. Research depends on the ability of independent validation of the presented results. In the case of the Sentinel-1 satellites, every researcher has access to the same base dataset, and therefore independent validation is possible. In this paper a method based on deep learning approach has been proposed. This approach takes as input Sentinel-1 images contain five important crops in Denmark. The suggested model could train from scratch and the average accuracy was obtained 85.4 percent.
OriginalsprogEngelsk
Udgivelsesår11 jul. 2019
StatusUdgivet - 11 jul. 2019

Se relationer på Aarhus Universitet Citationsformater

ID: 160685746