Título :	Deep semantic segmentation of center pivot irrigation systems from remotely sensed data
Autor :	Albuquerque, Anesmar Olino de Carvalho Júnior, Osmar Abílio de Carvalho, Osmar Luiz Ferreira de Bem, Pablo Pozzobon de Ferreira, Pedro Henrique Guimarães Moura, Rebeca dos Santos de Silva, Cristiano Rosa Gomes, Roberto Arnaldo Trancoso Guimarães, Renato Fontes
metadata.dc.identifier.orcid:	https://orcid.org/ 0000-0003-1561-7583 https://orcid.org/ 0000-0002-0346-1684 https://orcid.org/ 0000-0003-3868-8704 https://orcid.org/ 0000-0003-4724-4064 https://orcid.org/ 0000-0002-9555-043X
Assunto::	Irrigação Aprendizagem profunda Redes neurais convolucionais
Fecha de publicación :	6-jul-2020
Editorial :	MDPI
Citación :	ALBUQUERQUE, Anesmar Olino de et al. Deep semantic segmentation of center pivot irrigation systems from remotely sensed data. Remote Sensing, v. 12, n. 13, 2159, 2020. DOI: https://doi.org/10.3390/rs12132159. Disponível em: https://www.mdpi.com/2072-4292/12/13/2159. Acesso em: 23 nov. 2021.
Abstract:	The center pivot irrigation system (CPIS) is a modern irrigation technique widely used in precision agriculture due to its high efficiency in water consumption and low labor compared to traditional irrigation methods. The CPIS is a leader in mechanized irrigation in Brazil, with growth forecast for the coming years. Therefore, the mapping of center pivot areas is a strategic factor for the estimation of agricultural production, ensuring food security, water resources management, and environmental conservation. In this regard, digital processing of satellite images is the primary tool allowing regional and continuous monitoring with low costs and agility. However, the automatic detection of CPIS using remote sensing images remains a challenge, and much research has adopted visual interpretation. Although CPIS presents a consistent circular shape in the landscape, these areas can have a high internal variation with different plantations that vary over time, which is difficult with just the spectral behavior. Deep learning using convolutional neural networks (CNNs) is an emerging approach that provokes a revolution in image segmentation, surpassing traditional methods, and achieving higher accuracy and efficiency. This research aimed to evaluate the use of deep semantic segmentation of CPIS from CNN-based algorithms using Landsat-8 surface reflectance images (seven bands). The developed methodology can be subdivided into the following steps: (a) Definition of three study areas with a high concentration of CPIS in Central Brazil; (b) acquisition of Landsat-8 images considering the seasonal variations of the rain and drought periods; (c) definition of CPIS datasets containing Landsat images and ground truth mask of 256×256 pixels; (d) training using three CNN architectures (U-net, Deep ResUnet, and SharpMask); (e) accuracy analysis; and (f) large image reconstruction using six stride values (8, 16, 32, 64, 128, and 256). The three methods achieved state-of-the-art results with a slight prevalence of U-net over Deep ResUnet and SharpMask (0.96, 0.95, and 0.92 Kappa coefficients, respectively). A novelty in this research was the overlapping pixel analysis in the large image reconstruction. Lower stride values had improvements quantified by the Receiver Operating Characteristic curve (ROC curve) and Kappa, and fewer errors in the frame edges were also perceptible. The overlapping images significantly improved the accuracy and reduced the error present in the edges of the classified frames. Additionally, we obtained greater accuracy results during the beginning of the dry season. The present study enabled the establishment of a database of center pivot images and an adequate methodology for mapping the center pivot in central Brazil.
Licença::	(CC BY) © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
DOI:	https://doi.org/10.3390/rs12132159
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