Automatic Identification of Center Pivot Irrigation Systems from Landsat Images Using Convolutional Neural Networks

Chenxiao Zhang, Peng Yue, Liping Di and Zhaoyan Wu
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Chenxiao Zhang: State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
Peng Yue: School of Remote Sensing and Information Engineering, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
Liping Di: Center for Spatial Information Science and Systems (CSISS), George Mason University, 4400 University Drive, Fairfax, VA 22030, USA
Zhaoyan Wu: School of Remote Sensing and Information Engineering, Wuhan University, 129 Luoyu Road, Wuhan 430079, China

Agriculture, 2018, vol. 8, issue 10, 1-19

Abstract: Being hailed as the greatest mechanical innovation in agriculture since the replacement of draft animals by the tractor, center pivot irrigation systems irrigate crops with a significant reduction in both labor and water needs compared to traditional irrigation methods, such as flood irrigation. In the last few decades, the deployment of center pivot irrigation systems has increased dramatically throughout the United States. Monitoring the installment and operation of the center pivot systems can help: (i) Water resource management agencies to objectively assess water consumption and properly allocate water resources, (ii) Agro-businesses to locate potential customers, and (iii) Researchers to investigate land use change. However, few studies have been carried out on the automatic identification and location of center pivot irrigation systems from satellite images. Growing rapidly in recent years, machine learning techniques have been widely applied on image recognition, and they provide a possible solution for identification of center pivot systems. In this study, a Convolutional Neural Networks (CNNs) approach was proposed for identification of center pivot irrigation systems. CNNs with different structures were constructed and compared for the task. A sampling approach was presented for training data augmentation. The CNN with the best performance and less training time was used in the testing area. A variance-based approach was proposed to further locate the center of each center pivot system. The experiment was applied to a 30-m resolution Landsat image, covering an area of 20,000 km 2 in North Colorado. A precision of 95.85% and a recall of 93.33% of the identification results indicated that the proposed approach performed well in the center pivot irrigation systems identification task.

Keywords: center pivot irrigation systems; machine learning; convolutional neural networks; remote sensing (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2018
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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