17. Monz, B., et al., 2015. Sequential deep learning for human action
recognition,
Proceedings of the International Workshop on
Human Behavior Understanding
, pp. 29–39.
18. Parascandolo, G., et al., 2016. Recurrent neural networks for
polyphonic sound event detection in real life recordings,
Proceedings of the IEEE International Conference on Acoustics,
Speech and Signal Processing (ICASSP)
, pp. 6440–6444.
19. Rascher, U., et al., 2010. Sensing of Photosynthetic Activity of
Crops,
Precision Crop Protection - the Challenge and Use of
Heterogeneity
, pp. 87–99.
20. Richards, R., et al., 2010. Breeding for improved water
productivity in temperate cereals: phenotyping, quantitative trait
loci, markers and the selection environment,
Functional Plant
Biology
, 37(2):8597.
21. Römer, C., et al., 2006. Early drought stress detection in cereals:
Simplex volume maximisation for hyperspectral image analysis,
Plant Physiology
, 39(11):10651074.
22. Ruck, D., et al., 1999. The multilayer perceptron as an
approximation to a Bayes optimal discriminant function,
IEEE
Transactions on Neural Networks
, 1(4):296–298.
23. Russakovsky, O., et al., 2014. ImageNet Large Scale Visual
Recognition Challenge,
arXiv preprint arXiv
:1409.0575.
24. Schuster, M., et al., 1997. Bidirectional recurrent neural networks,
IEEE Transactions on Signal Processing
, 45(11):2673–2681.
25. Simonyan, K., et al., 1999. Learning to forget: Continual
prediction with LSTM,
Proceedings of the International
Conference on Artificial Neural Networks
, pp. 850–855.
26. Simonyan, K., et al., 2014. Very Deep Convolutional Networks for
Large-Scale Image Recognition,
arXiv preprint arXiv
:1409.1556.
27. Somerville, C., et al., 2001. Genetic engineering and water,
International Science,
2217–2217.
28. Thenkabail, P., et al., 2000. Hyperspectral vegetation indices and
their relationships with agricultural crop characteristics,
Remote
Sensing of Environment
, 71(2):158–182.
29. Tilling, A., et al., 2007. Remote sensing of nitrogen and water
stress in wheat,
Field Crops Research
, 104(1-3):77–85.
30. Ustin, S., et al., 2010. Remote sensing of plant functional types,
New Phytologist
, 186(4):795–816.
31. Virlet, N., et al., 2016. Field Scanalyzer: An automated robotic
field phenotyping platform for detailed crop monitoring,
Functional Plant Biology
, 44(1):143–153.
32. Wang, B., et al., 2015. Learning natural language inference with
LSTM,
arXiv preprint arXiv
:1512.08849.
33. Wen, J., et al., 2015. Semantically conditioned lstm-based natural
language generation for spoken dialogue systems,
arXiv preprint
arXiv
:1508.01745.
34. Xiong, L., et al., 2006. Identification of drought tolerance
determinants by genetic analysis of root response to drought
stress and abscisic acid,
Plant Physiology
, 142(3):10651074.
35. Yilmaz, M., et al., 2008. Remote sensing of vegetation water
content from equivalent water thickness using satellite imagery,
Remote Sensing of Environment
, 112(5):25142522.
36. Yu, J., et al., 2008. Genetic design and statistical power of nested
association mapping in maize, Genetics, 178(1):539–551.
468
July 2018
PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING
