The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B2-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 1337–1342, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-1337-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 1337–1342, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-1337-2020

  14 Aug 2020

14 Aug 2020

APPLICATION OF TEMPORAL CONVOLUTIONAL NEURAL NETWORK FOR THE CLASSIFICATION OF CROPS ON SENTINEL-2 TIME SERIES

M. Račič1, K. Oštir1, D. Peressutti2, A. Zupanc2, and L. Čehovin Zajc3 M. Račič et al.
  • 1Faculty of civil and geodetic engineering, University of Ljubljana, Slovenia
  • 2Sinergise d.o.o., Ljubljana, Slovenia
  • 3Faculty of Computer and Information Science, University of Ljubljana, Slovenia

Keywords: deep learning, multi-temporal classification, sequence data, crop classification, Sentinel-2

Abstract. The recent development of Earth observation systems – like the Copernicus Sentinels – has provided access to satellite data with high spatial and temporal resolution. This is a key component for the accurate monitoring of state and changes in land use and land cover. In this research, the crops classification was performed by implementing two deep neural networks based on structured data. Despite the wide availability of optical satellite imagery, such as Landsat and Sentinel-2, the limitations of high quality tagged data make the training of machine learning methods very difficult. For this purpose, we have created and labeled a dataset of the crops in Slovenia for the year 2017. With the selected methods we are able to correctly classify 87% of all cultures. Similar studies have already been carried out in the past, but are limited to smaller regions or a smaller number of crop types.