Semantic Image Segmentation of Agricultural Field Problem Areas Using Deep Neural Networks Based on the DeepLabV3 Model-Reference-Cited by-同舟云学术

Semantic Image Segmentation of Agricultural Field Problem Areas Using Deep Neural Networks Based on the DeepLabV3 Model

Published:2023 Issue: Volume: Page:449-461
ISSN:2367-3370
Container-title:Fourth International Conference on Image Processing and Capsule Networks
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Author:

Rogachev Aleksey,Belousov Ilya,Rogachev Dmitry

Publisher

Springer Nature Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-99-7093-3_30

Reference26 articles.

1. Saiz-Rubio V From smart farming towards agriculture 5.0: a review on crop data management. https://www.mdpi.com/2073-4395/10/2/207/htm

2. Alberto G-G, Sergio O-E, Sergiu O, Victor V-M, Jose G-R A review on deep learning techniques applied to semantic segmentation. https://doi.org/10.48550/arXiv.1704.06857

3. Badrinarayanan V, Kendall A, Cipolla R SegNet: a deep convolutional encoder-decoder architecture for image segmentation. http://docs.cntd.ru/document/902361843

4. Xu B et al (2004) Remote sensing monitoring on dynamic status of grassland productivity and animal loading balance in Northern China. In: IGARSS 2004. 2004 IEEE international geoscience and remote sensing symposium, Anchorage, AK, USA, vol 4, pp 2306–2309. https://doi.org/10.1109/IGARSS.2004.1369747

5. Pelletier C, Valero S, Inglada J, Champion N, Dedieu G (2016) Assessing the robustness of Random Forests to map land cover with high resolution satellite image time series over large areas. Rem Sens Environ 187:156–168

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