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Article

Mapping Center Pivot Irrigation Systems in the Southern Amazon from Sentinel-2 Images

1
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
2
School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 101408, China
3
CNRS, UMR 6554 LETG, 35043 Rennes, France
*
Author to whom correspondence should be addressed.
Academic Editors: Michael T. Coe, Marcia N. Macedo and Michael Lathuillière
Water 2021, 13(3), 298; https://doi.org/10.3390/w13030298
Received: 10 October 2020 / Revised: 21 January 2021 / Accepted: 22 January 2021 / Published: 26 January 2021
Irrigation systems play an important role in agriculture. Center pivot irrigation systems are popular in many countries as they are labor-saving and water consumption efficient. Monitoring the distribution of center pivot irrigation systems can provide important information for agricultural production, water consumption and land use. Deep learning has become an effective method for image classification and object detection. In this paper, a new method to detect the precise shape of center pivot irrigation systems is proposed. The proposed method combines a lightweight real-time object detection network (PVANET) based on deep learning, an image classification model (GoogLeNet) and accurate shape detection (Hough transform) to detect and accurately delineate center pivot irrigation systems and their associated circular shape. PVANET is lightweight and fast and GoogLeNet can reduce the false detections associated with PVANET, while Hough transform can accurately detect the shape of center pivot irrigation systems. Experiments with Sentinel-2 images in Mato Grosso achieved a precision of 95% and a recall of 95.5%, which demonstrated the effectiveness of the proposed method. Finally, with the accurate shape of center pivot irrigation systems detected, the area of irrigation in the region was estimated. View Full-Text
Keywords: center pivot irrigation systems; water resources; object detection; image recognition; deep learning; convolutional neural network; Hough transform center pivot irrigation systems; water resources; object detection; image recognition; deep learning; convolutional neural network; Hough transform
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MDPI and ACS Style

Tang, J.; Arvor, D.; Corpetti, T.; Tang, P. Mapping Center Pivot Irrigation Systems in the Southern Amazon from Sentinel-2 Images. Water 2021, 13, 298. https://doi.org/10.3390/w13030298

AMA Style

Tang J, Arvor D, Corpetti T, Tang P. Mapping Center Pivot Irrigation Systems in the Southern Amazon from Sentinel-2 Images. Water. 2021; 13(3):298. https://doi.org/10.3390/w13030298

Chicago/Turabian Style

Tang, Jiwen, Damien Arvor, Thomas Corpetti, and Ping Tang. 2021. "Mapping Center Pivot Irrigation Systems in the Southern Amazon from Sentinel-2 Images" Water 13, no. 3: 298. https://doi.org/10.3390/w13030298

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