![](bg8.png)
In our comparative study, we notice that the Tem-
pCNN method (Pelletier et al., 2019) is applied at
the pixel level while our approach is applied at the
polygon level. This means that for TempCNN there
are more training samples compared to our method
where the pixels of a polygon are all summed up in
a spatio-temporal image. Despite the low number of
data available, the accuracy increase made possible by
our process is up to 8% based on the original data and
5% on the interpolated data.
In future works, the ordering of the pixels will
be more precisely studied, till now we have ordered
the pixels of a square region where the polygon is in-
cluded but we have to define an order adapted to the
geometry of the polygon itself. We will also increase
the number of instances of orchards and also apply the
same approach to problems involving a larger number
of classes in order to generate land-cover maps.
ACKNOWLEDGEMENTS
The French ANR supported this work under Grant
ANR-17-CE23-0015.
REFERENCES
Andres, L., Salas, W., and Skole, D. (1994). Fourier anal-
ysis of multi-temporal AVHRR data applied to a land
cover classification. Int. J. Remote Sens., 15(5):1115–
1121.
Bagnall, A., Lines, J., Bostrom, A., Large, J., and Keogh,
E. (2017). The great time series classification bake
off: a review and experimental evaluation of recent
algorithmic advances. DMKD, 31(3):606–660.
Bruzzone, L. and Prieto, D. (2000). Automatic analysis of
the difference image for unsupervised change detec-
tion. IEEE Transactions on Geoscience and Remote
Sens., 38(3):1171–1182.
Butz, A. (1971). Alternative algorithm for Hilbert’s space-
filling curve. IEEE Trans. on Computers, 20(4):424–
426.
Chelali, M., Kurtz, C., Puissant, A., and Vincent, N. (2019).
Urban land cover analysis from satellite image time
series based on temporal stability. In JURSE, Procs.,
pages 1–4.
Di Mauro, N., Vergari, A., Basile, T. M. A., Ventola,
F. G., and Esposito, F. (2017). End-to-end learning of
deep spatio-temporal representations for satellite im-
age time series classification. In DC@PKDD/ECML,
Procs., pages 1–8.
Huang, B., Lu, K., Audebert, N., Khalel, A., Tarabalka, Y.,
Malof, J., and Boulch, A. (2018). Large-scale seman-
tic classification: Outcome of the first year of inria
aerial image labeling benchmark. In IGARSS, Procs.,
pages 6947–6950.
Iandola, F., Moskewicz, M., Ashraf, K., Han, S., Dally,
W., and Keutzer, K. (2016). Squeezenet: AlexNet-
level accuracy with 50x fewer parameters and <1MB
model size. CoRR, abs/1602.07360.
Ienco, D., Gaetano, R., Dupaquier, C., and Maurel, P.
(2017). Land cover classification via multitemporal
spatial data by deep recurrent neural networks. IEEE
Geosci. Remote Sens. Lett., 14(10):1685–1689.
Inglada, J., Vincent, A., Arias, M., Tardy, B., Morin, D.,
and Rodes, I. (2017). Operational high resolution land
cover map production at the country scale using satel-
lite image time series. Remote Sens., 9(1):95–108.
Ismail Fawaz, H., Forestier, G., Weber, J., Idoumghar, L.,
and Muller, P. (2019). Deep learning for time series
classification: A review. DMKD, 33(4):917–963.
Pelletier, C., Webb, G., and Petitjean, F. (2019). Temporal
convolutional neural network for the classification of
satellite image time series. Remote Sens., 11(5):523–
534.
Petitjean, F., Inglada, J., and Ganc¸arski, P. (2012a). Satel-
lite image time series analysis under time warping.
IEEE Transactions on Geoscience and Remote Sens.,
50(8):3081–3095.
Petitjean, F., Kurtz, C., Passat, N., and Ganc¸arski, P.
(2012b). Spatio-temporal reasoning for the classifica-
tion of satellite image time series. PRL, 33(13):1805–
1815.
Senf, C., Leitao, P., Pflugmacher, D., Van der Linden,
S., and Hostert, P. (2015). Mapping land cover
in complex mediterranean landscapes using landsat:
Improved classification accuracies from integrating
multi-seasonal and synthetic imagery. Remote Sens.
Environ., 156:527–536.
Tran, D., Bourdev, L., Fergus, R., Torresani, L., and Paluri,
M. (2015). Learning spatiotemporal features with 3D
convolutional networks. In ICCV, Procs., pages 4489–
4497.
Verbesselt, J., Hyndman, R., Newnham, G., and Culvenor,
D. (2010). Detecting trend and seasonal changes in
satellite image time series. Remote Sens. Environ.,
114(1):106–115.
Image Time Series Classification based on a Planar Spatio-temporal Data Representation
283