Semantic segmentation of satellite images for crop type identification in smallholder farms
Author:
Funder
European Space Agency
Publisher
Springer Science and Business Media LLC
Subject
Hardware and Architecture,Information Systems,Theoretical Computer Science,Software
Link
https://link.springer.com/content/pdf/10.1007/s11227-023-05533-4.pdf
Reference71 articles.
1. You J, Li X, Low M, et al (2017) Deep gaussian process for crop yield prediction based on remote sensing data. In: Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI-17). pp 4559–4565
2. Rembold F, Atzberger C, Savin I, Rojas O (2013) Using low resolution satellite imagery for yield prediction and yield anomaly detection. Remote Sens Environ 5:1704–1733. https://doi.org/10.3390/rs5041704
3. Mohanty SP, Czakon J, Kaczmarek KA et al (2020) Deep learning for understanding satellite imagery: an experimental survey. Front Artif Intell 3:1–21. https://doi.org/10.3389/frai.2020.534696
4. Wang P, Chen P, Yuan Y, et al (2018) Understanding convolution for semantic segmentation. In: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV). pp 1451–1460
5. Quarmby NA, Milnes M, Hindle TL, Silleos N (1993) The use of multi-temporal NDVI measurements from AVHRR data for crop yield estimation and prediction. Int J Remote Sens 14:199–210. https://doi.org/10.1080/01431169308904332
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