Deep Recurrent Neural Networks for Winter Vegetation Quality Mapping via Multitemporal SAR Sentinel-1

IEEE Geoscience and Remote Sensing Letters

Volumn 15, Issue 3, 2018, Pages 465-468

  Ho Tong Minh, Dinh ^a   Ienco, Dino ^a,b   Gaetano, Raffaele ^a   Lalande, Nathalie ^c   Ndikumana, Emile ^a   Osman, Faycal ^a   Maurel, Pierre ^a  

^a UNIV MONTPELLIER   (France)

^b LIRMM   (France)

^c Envylis Cie   (France)

Author keywords

Gated recurrent unit (GRU); long short term memory (LSTM); multitemporal; recurrent neural network (RNN); Sentinel 1; synthetic aperture radar (SAR); vegetation quality

Indexed keywords

DECISION TREES; DEEP NEURAL NETWORKS; GEOMETRICAL OPTICS; MAPPING; RADAR IMAGING; REMOTE SENSING; SYNTHETIC APERTURE RADAR; VEGETATION;

GATED RECURRENT UNIT (GRU); LEARNING TECHNIQUES; MACHINE LEARNING APPROACHES; MULTI-TEMPORAL; MULTI-TEMPORAL SAR; QUALITY MAPPING; RECURRENT NEURAL NETWORK (RNN); SENTINEL-1;

LONG SHORT-TERM MEMORY;

ARTIFICIAL NEURAL NETWORK; CLOUD COVER; MACHINE LEARNING; OPTICAL PROPERTY; REMOTE SENSING; SENTINEL; SYNTHETIC APERTURE RADAR; VEGETATION COVER;

CANADA; FRANCE; MARITIME PROVINCES;

EID: 85041386709     PISSN: 1545598X     EISSN: 15580571     Source Type: Journal    
DOI: 10.1109/LGRS.2018.2794581     Document Type: Article

References (20)

1. G. Kallis and D. Butler, "The EU water framework directive: Measures and implications," Water Policy, vol. 3, no. 2, pp. 125-142, 2001.
2. C. Buckley and P. Carney, "The potential to reduce the risk of diffuse pollution from agriculture while improving economic performance at farm level," Environ. Sci. Policy, vol. 25, no. 1, pp. 118-126, 2013.
3. W. D. Hively, M. Lang, G. W. McCarty, J. Keppler, A. Sadeghi, and L. L. McConnell, "Using satellite remote sensing to estimate winter cover crop nutrient uptake efficiency," J. Soil Water Conservation, vol. 64, no. 5, pp. 303-313, 2009.
4. R. Torres et al., "GMES Sentinel-1 mission," Remote Sens. Environ., vol. 120, pp. 9-24, May 2012.
5. J. Inglada, A. Vincent, M. Arias, and C. Marais-Sicre, "Improved early crop type identification by joint use of high temporal resolution SAR and optical image time series," Remote Sens., vol. 8, no. 5, p. 362, 2016.
6. Y. Bengio, A. Courville, and P. Vincent, "Representation learning: A review and new perspectives," IEEE Trans. Pattern Anal. Mach. Intell., vol. 35, no. 8, pp. 1798-1828, Aug. 2013.
7. S. Hochreiter and J. Schmidhuber, "LSTM can solve hard long time lag problems," in Proc. NIPS, 1996, pp. 473-479.
8. P. Prats-Iraola, R. Scheiber, L. Marotti, S. Wollstadt, and A. Reigber, "TOPS interferometry with TerraSAR-X," IEEE Trans. Geosci. Remote Sens., vol. 50, no. 8, pp. 3179-3188, Aug. 2012.
9. S. Quegan and J. J. Yu, "Filtering of multichannel SAR images," IEEE Trans. Geosci. Remote Sens., vol. 39, no. 11, pp. 2373-2379, Nov. 2001.
10. D. H. T. Minh, Y.-N. Ngo, N. Baghdadi, and P. Maurel, "TomoSAR platform: The new Irstea service as demand for SAR, interferometry, polarimetry and tomography," in Proc. ESA Living Planet Symp., 2016, pp. 1-8.
11. K. Soma, R. Mori, R. Sato, N. Furumai, and S. Nara, "Simultaneous multichannel signal transfers via chaos in a recurrent neural network," Neural Comput., vol. 27, no. 5, pp. 1083-1101, 2015.
12. T. Linzen, E. Dupoux, and Y. Goldberg, "Assessing the ability of LSTMs to learn syntax-sensitive dependencies," in Proc. TACL, vol. 4. 2016, pp. 521-535.
13. K. Cho et al., "Learning phrase representations using RNN encoderdecoder for statistical machine translation," in Proc. EMNLP, 2014, pp. 1724-1734.
14. A. Graves, A.-R. Mohamed, and G. E. Hinton, "Speech recognition with deep recurrent neural networks," in Proc. ICASSP, May 2013, pp. 6645-6649.
15. R. Flamary, M. Fauvel, M. D. Mura, and S. Valero, "Analysis of multitemporal classification techniques for forecasting image time series," IEEE Geosci. Remote Sens. Lett., vol. 12, no. 5, pp. 953-957, May 2015.
16. F. Pedregosa et al., "Scikit-learn: Machine learning in Python," J. Mach. Learn. Res., vol. 12, pp. 2825-2830, Oct. 2011.
17. C.-C. Chang and C.-J. Lin, "LIBSVM: A library for support vector machines," ACM Trans. Intell. Syst. Technol., vol. 2, no. 3, pp. 27:1-27:27, 2011.
18. F. Chollet. (2015). keras. [Online]. Available: https://github.com/fchollet/ keras
19. Y. N. Dauphin, H. de Vries, J. Chung, and Y. Bengio, "RMSProp and equilibrated adaptive learning rates for nonconvex optimization," unpublished paper, 2015. [Online]. Available: https://arxiv.org/abs/1502.04390v1
20. L. Zhang, L. Zhang, and B. Du, "Deep learning for remote sensing data: A technical tutorial on the state of the art," IEEE Geosci. Remote Sens. Mag., vol. 4, no. 2, pp. 22-40, Jun. 2016.