Large-scale automatic extraction of agricultural greenhouses based on high-resolution remote sensing and deep learning technologies
Author:
Funder
Key Technologies Research and Development Program
Publisher
Springer Science and Business Media LLC
Subject
Health, Toxicology and Mutagenesis,Pollution,Environmental Chemistry,General Medicine
Link
https://link.springer.com/content/pdf/10.1007/s11356-023-29802-0.pdf
Reference57 articles.
1. Aguera F, Liu JG (2009) Automatic greenhouse delineation from QuickBird and Ikonos satellite images. Comput Electron Agric 66:191–200
2. Aguilar MA, Jimenez-Lao R, Ladisa C, Aguilar FJ, Tarantino E (2022) Comparison of spectral indices extracted from Sentinel-2 images to map plastic covered greenhouses through an object-based approach. Gisci Remote Sens 59:822–842
3. Aguilar MA, Nemmaoui A, Novelli A, Aguilar FJ, Garcia Lorca A (2016a) Object-based greenhouse mapping using very high resolution satellite data and Landsat 8 time series. Remote Sens 8
4. Aguilar MA, Nemmaoui A, Novelli A, Aguilar FJ, Lorca AG (2016b) Object-based greenhouse mapping using very high resolution satellite data and Landsat 8 time series. Remote Sens 8
5. Aguilar MA, Jimenez-Lao R, Nemmaoui A, Jose Aguilar F, Koc-San D, Tarantino E, Chourak M (2020) Evaluation of the consistency of simultaneously acquired Sentinel-2 and Landsat 8 imagery on plastic covered greenhouses. Remote Sens 12
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