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Applying deep learning for agricultural classification using multitemporal SAR Sentinel-1 for Camargue, France
Author(s): Emile Ndikumana; Dinh Ho Tong Minh; Nicolas Baghdadi; Dominique Courault ; Laure Hossard
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Paper Abstract

The aim of this paper is to provide a better understanding of potentialities of the new Sentinel-1 radar images for mapping the different crops in the Camargue region in the South France. The originality relies on deep learning techniques. The analysis is carried out on multitemporal Sentinel-1 data over an area in Camargue,France.50 Sentinel-1 images processed in order to produce an intensity radar data stack from May 2017 to September 2017. We revealed that even with classical machine learning approaches (K nearest neighbors, random forest, and support vector machine), good performance classification could be achieved with F-measure/Accuracy greater than 86 % and Kappa coefficient better than 0.82. We found that the results of the two deep recurrent neural network (RNN)-based classifiers clearly outperformed the classical approaches. Finally, our analyses of Camargue area results show that the same performance was obtained with two different RNN-based classifiers on the Rice class, which is the most dominant crop of this region, with a F-measure metric of 96 %. These results thus highlight that in the near future, these RNN-based techniques will play an important role in the analysis of remote sensing time series.

Paper Details

Date Published: 9 October 2018
PDF: 12 pages
Proc. SPIE 10789, Image and Signal Processing for Remote Sensing XXIV, 1078911 (9 October 2018); doi: 10.1117/12.2325160
Show Author Affiliations
Emile Ndikumana, UMR TETIS, IRSTEA, Univ. of Montpellier (France)
Dinh Ho Tong Minh, UMR TETIS, IRSTEA, Univ. of Montpellier (France)
Nicolas Baghdadi, UMR TETIS, IRSTEA, Univ. of Montpellier (France)
Dominique Courault , INRA, Univ. of Avignon (France)
Laure Hossard, INRA, Univ. of Montpellier (France)


Published in SPIE Proceedings Vol. 10789:
Image and Signal Processing for Remote Sensing XXIV
Lorenzo Bruzzone; Francesca Bovolo, Editor(s)

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